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Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! The AI Research team at Figma is working on the cutting edge of AI and Design. We work on optimizing agentic systems for design quality and end user experience. We build robust evaluators for Design and post-train models. We are a very collaborative, close-knit and impact-focused team of researchers and research engineers working on some of the most challenging technical problems at the intersection of AI and creativity. We are seeking PhD interns with expertise in machine learning and artificial intelligence to develop innovative AI solutions for design and creative products. Through both fundamental and applied research, your work will contribute to new AI-driven features in Figma products. This internship can take place in Winter 2027 or Summer 2027 in San Francisco or New York. What you’ll do at Figma: Drive fundamental and applied research in Generative AI with Figma product use cases in mind Formulate and implement new modeling approaches both to improve the effectiveness of Figma’s current AI models as well as enable the launch of entirely new AI-powered product features Explore the boundaries of what is possible with the current technology set and experiment with novel ideas Depending on your project, you may have the opportunity to publish your research in leading AI/ML conferences We'd love to hear from you if you have: Are a 4th or 5th year senior Ph.D student in AI, Machine Learning, Computer Vision, Natural Language Processing, Human Computer Interaction or a related field Proven expertise in AI through publications on recent advancements in AI, such as Large Language Models or Agentic systems Track record in applying machine learning techniques to projects in academia or industry Experience in Python and machine learning frameworks (such as PyTorch, TensorFlow) Experience solving sophisticated problems and comparing alternative solutions, trade-offs, and diverse points of view to determine a path forward While it’s not required, it’s an added plus if you also have: Related industry experience via a past internship or full-time role Experience communicating your solutions effectively to different audiences Experience using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, Claude) to write, debug, and optimize code At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure This internship role is based in either Figma’s San Francisco or New York hub offices, and has the hourly base pay rate stated below. Figma also offers interns a housing stipend and travel reimbursement. Figma’s compensation and benefits are subject to change and may be modified in the future. Internship $74 — $74 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice .
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! We are looking for an experienced Data Scientist to join our growing data team. At Figma, Data Scientists are deeply embedded within cross-functional teams across the company—from Product to Finance, Marketing, and Platform. We are hiring across a number of roles in these areas. This is ideal for someone excited to own high-impact data projects, partner strategically with stakeholders, and shape the future of our products and business. Figma is uniquely positioned to harness advances in AI while keeping people at the heart of creative product development. As part of our Data Science team, you'll help address the most pressing challenges emerging from AI-driven development, shape next-generation data science workflows, and build AI-powered tools used by millions of product teams around the world. The ideal candidate will bring strong analytical and technical skills, business intuition, and a collaborative mindset to guide decision-making and unlock growth opportunities. You'll work on problems ranging from understanding user behavior to optimizing revenue strategies, improving internal tooling, and influencing product direction through experimentation and data modeling. This is a full-time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: Collaborate across teams to define and measure key metrics, design experiments, and uncover insights that inform strategic decisions Build models and analytical frameworks to support product, marketing, platform, or finance initiatives Develop tools, datasets, and systems that enable others to work with data more efficiently and rigorously Own complex data projects end-to-end, from framing the business question through to shipping a solution stakeholders act on Champion data quality, accessibility, and the democratization of data across the organization Partner with Product, Engineering, Design, Research, Sales, Marketing, or Finance to drive impact We'd love to hear from you if you have: 4+ years of experience in Analytics, Data Science, or a related field Fluency in SQL and proficiency in a scripting language like Python or R, along with experience working with distributed data systems (e.g., Redshift, Snowflake, Presto, Hive, Spark) Strong foundation in statistical methods, experimentation, and/or forecasting A track record of working cross-functionally and communicating effectively with both technical and non-technical partners Experience supporting one or more of the following: Product, Marketing, Finance, or internal Platform/Tooling teams While it's not required, it's an added plus if you also have: Comfortable defining your own priorities and adapting quickly as priorities shift At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $140,000 — $348,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice .
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma is growing our Finance Data Science team at a pivotal moment in the company's evolution. As a public company, the accuracy, scalability, and sophistication of our financial data systems have never been more important. At the same time, Figma's business continues to evolve through new products, AI-powered offerings, acquisitions, and emerging revenue models. We're looking for a Finance Data Scientist to help build the data foundation that powers financial reporting, forecasting, strategic decision-making, and investor communications. In this role, you'll partner closely with Strategic Finance, Accounting, Investor Relations, Engineering, Product, and Data teams to ensure our financial metrics are accurate, actionable, and trusted across the business. This is a unique opportunity to work at the intersection of finance, analytics, data engineering, and business strategy—helping shape how Figma measures and communicates its performance as the company scales. The data and systems built by this team directly support executive decision-making, public company reporting, and Figma's long-term growth strategy. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: Own and improve the data models that power Figma's financial reporting, including ARR, revenue, billings, collections, and other key business metrics Build scalable reporting systems, forecasts, and analytical frameworks that support Finance, Accounting, Investor Relations, and executive leadership Drive data accuracy and completeness across month-end, quarter-end, and year-end close processes alongside Strategic Finance and Accounting teams Collaborate with Engineering, Product, GTM Systems, and Financial Systems teams to ensure the completeness and accuracy of financial data Conduct deep-dive analyses that inform strategic decisions and uncover opportunities to improve business performance Define, measure, and operationalize key financial and operational metrics Design and implement durable data solutions that balance immediate stakeholder needs with long-term scalability Serve as a trusted thought partner to Finance leaders, helping guide reporting, forecasting, and decision-making across the business We'd love to hear from you if you have: 3+ years of experience in Data Science, Analytics, Finance, Financial Systems, Data Engineering, or a related field Advanced SQL skills and strong proficiency in Python or a similar programming language Experience supporting Finance, Accounting, Strategic Finance, FP&A, Investor Relations, or other finance-focused stakeholders Demonstrated ability to translate complex data into clear, actionable business insights for non-technical stakeholders Experience working independently and driving projects from ambiguity to execution While it's not required, it's an added plus if you also have: Experience with SaaS, subscription-based, usage-based, or consumption-based business models Knowledge of accounting concepts and revenue recognition principles (ASC 606) Experience with modern analytics and data engineering tools such as DBT, Snowflake, Git, Spark, Presto, or similar technologies Experience supporting IPO readiness, public company reporting, SOX controls, audits, or other major corporate milestones Background in Strategic Finance, FP&A, Accounting, Investment Banking, Consulting, or related fields At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $140,000 — $348,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice .
Who we are About Stripe Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. More than 10 million businesses build on Stripe, spanning the economic frontier—from solo founders to established enterprises—united by a practical focus on growth. The most ambitious companies in the world use Stripe as core infrastructure to grow faster. They process trillions of dollars a year on Stripe, equivalent to around 1.6% of global GDP. While economic growth makes everyone better off, open markets also enable greater variety. When any business can easily serve a global customer base, the quality and diversity of products in the world increase, and craft and creativity are unleashed into the smallest niches. Our own growth is wholly contingent on the success of the businesses building on Stripe. We therefore invest back into our technology at an unusual rate. We make upgrades to our products every single day to deliver compounding gains to our customers. We maintain some of the most reliable APIs on the internet. We build entirely new pieces of financial infrastructure to enable new ideas. And our significant advances in risk and fraud infrastructure over many years are making the internet economy safer and more accessible. Though people at Stripe don’t tend to take themselves seriously, Stripe is a fairly serious place: our customers are depending on us for their livelihoods. We admire ambition, intensity, curiosity, humility, and rigor. The most effective people become knowledgeable about many domains besides their own. Any company is an applied exercise in understanding some aspect of society or the market. In working with so many (especially the new and innovative ones), we think that Stripe is one of the very best places to learn about how the world works. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust. What you’ll do As an intern at Stripe you’ll work on projects across our stack that directly impact the way millions of businesses operate. You’ll own problems end to end with the support of your manager and teammates. It’s an opportunity to work alongside some of the most creative and technically rigorous data analysts and scientists in the industry. You'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling. Our internship program is competitive and the expectations are high. Responsibilities Applying probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes Using Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses Building, training, and evaluating predictive models across regression and classification tasks for bias-variance trade-offs and model selection Modeling temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions Identifying structural patterns, clusters, and outliers in unlabeled data Deploying models in production and adjusting model thresholds to improve performance Designing, running, and analyzing complex experiments and leveraging causal inference designs Using SQL and Spark to create, transform, and analyze large datasets Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly Present your work to the Data Science team, partner teams, and fellow interns. Who you are Ambitious builder: You’re energized by building solutions without clear precedent and solving problems with far-reaching consequences. Successful Stripes are deeply curious, and prefer the joy of discovery to the comfort of certainty. Rigorous thinker: You appreciate that things worth doing are rarely simple. You enjoy working on problems that have never been tackled before. Adaptable problem solver: You adapt quickly and treat obstacles as opportunities. At Stripe we embrace kindness while encouraging Stripes to take measured risks and act boldly, even in the absence of consensus. Minimum requirements Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028 Experience with SQL and a scientific computing language (such as Python, R, etc.) Proficiency with AI tools to accelerate model development, analysis, and coding Experience communicating and collaborating with multidisciplinary stakeholders in a team environment Preferred qualifications Experience writing and debugging data pipelines Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts
Who we are About Stripe Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. More than 10 million businesses build on Stripe, spanning the economic frontier—from solo founders to established enterprises—united by a practical focus on growth. The most ambitious companies in the world use Stripe as core infrastructure to grow faster. They process trillions of dollars a year on Stripe, equivalent to around 1.6% of global GDP. While economic growth makes everyone better off, open markets also enable greater variety. When any business can easily serve a global customer base, the quality and diversity of products in the world increase, and craft and creativity are unleashed into the smallest niches. Our own growth is wholly contingent on the success of the businesses building on Stripe. We therefore invest back into our technology at an unusual rate. We make upgrades to our products every single day to deliver compounding gains to our customers. We maintain some of the most reliable APIs on the internet. We build entirely new pieces of financial infrastructure to enable new ideas. And our significant advances in risk and fraud infrastructure over many years are making the internet economy safer and more accessible. Though people at Stripe don’t tend to take themselves seriously, Stripe is a fairly serious place: our customers are depending on us for their livelihoods. We admire ambition, intensity, curiosity, humility, and rigor. The most effective people become knowledgeable about many domains besides their own. Any company is an applied exercise in understanding some aspect of society or the market. In working with so many (especially the new and innovative ones), we think that Stripe is one of the very best places to learn about how the world works. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust. What you’ll do As an intern at Stripe you’ll work on projects across our stack that directly impact the way millions of businesses operate. You’ll own problems end to end with the support of your manager and teammates. It’s an opportunity to work alongside some of the most creative and technically rigorous data analysts and scientists in the industry. You'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling. Our internship program is competitive and the expectations are high. Responsibilities Applying probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes Using Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses Building, training, and evaluating predictive models across regression and classification tasks for bias-variance trade-offs and model selection Modeling temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions Identifying structural patterns, clusters, and outliers in unlabeled data Deploying models in production and adjusting model thresholds to improve performance Designing, running, and analyzing complex experiments and leveraging causal inference designs Using SQL and Spark to create, transform, and analyze large datasets Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly Present your work to the Data Science team, partner teams, and fellow interns. Who you are Ambitious builder: You’re energized by building solutions without clear precedent and solving problems with far-reaching consequences. Successful Stripes are deeply curious, and prefer the joy of discovery to the comfort of certainty. Rigorous thinker: You appreciate that things worth doing are rarely simple. You enjoy working on problems that have never been tackled before. Adaptable problem solver: You adapt quickly and treat obstacles as opportunities. At Stripe we embrace kindness while encouraging Stripes to take measured risks and act boldly, even in the absence of consensus. Minimum requirements Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028 Experience with SQL and a scientific computing language (such as Python, R, etc.) Proficiency with AI tools to accelerate model development, analysis, and coding Experience communicating and collaborating with multidisciplinary stakeholders in a team environment Preferred qualifications Experience writing and debugging data pipelines Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The Experimental Projects team quickly tests new product opportunities for Stripe. We work on brand-new, zero-to-one problems by building prototypes, talking with users, analyzing what we learn, and iterating rapidly. The team operates across a broad range of problem spaces. Rather than optimizing a single mature product area, you’ll help determine whether new ideas can solve meaningful user problems and become valuable products for Stripe. We’re looking for a Data Scientist who enjoys building, has a strong bias for action, and is comfortable moving from an ambiguous question to a practical test. Responsibilities Use data to identify, evaluate, and shape new product opportunities. Partner with engineers and product managers to build and test early product concepts. Develop analyses, models, experiments, and prototypes that help the team learn quickly. Talk with users and combine qualitative insights with quantitative evidence. Define success measures for new ideas and assess whether early results support further investment. Work across several new problem areas, adapting your approach as priorities and evidence change. Communicate findings clearly, including uncertainty, tradeoffs, and recommended next steps. Help establish analytical foundations for projects that may grow into larger product areas. What you'll do You’ll partner closely with product managers, engineers, designers, and other cross-functional partners to explore new product opportunities. You’ll use data science throughout the discovery and development process, from identifying promising problems and shaping hypotheses to building early solutions and evaluating results. Your work may include product analytics, experimentation, statistical modeling, machine learning, causal inference, and rapid prototyping. The specific methods will depend on the opportunity. Success in this role requires choosing the right level of analytical rigor for each stage, working quickly when evidence is limited, and turning what you learn into clear recommendations about what the team should build or test next. Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Location Requirement San Francisco CA, Seattle WA or New York, NY preferred (50% in office - hybrid) Minimum requirements PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience Proficiency in SQL and a computing language such as Python or R. Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results. A demonstrated ability to manage and deliver on multiple projects with a high attention to detail. Solid business acumen and experience in synthesizing complex analyses into actionable recommendations. A track record of building relationships with and influencing the decisions of senior technical leadership. A builder's mindset with a willingness to question assumptions and conventional wisdom. Proficiency with artificial intelligence tools to accelerate model development, analysis, and coding. Preferred qualifications Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation Experience deploying models in production and adjusting model thresholds to improve performance Experience designing, running, and analyzing complex experiments or using causal inference methods A builder’s mindset and willingness to question assumptions and conventional wisdom Experience working on ambiguous, zero-to-one problems and turning early evidence into practical decisions A strong bias for action, including the ability to identify the fastest credible way to test a hypothesis Comfort moving across different problem spaces and learning unfamiliar domains quickly Experience with distributed tools such as Spark or Hadoop A PhD or MS in a quantitative field, such as statistics, engineering, mathematics, economics, quantitative finance, science, or operations research
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background. What you'll do We're looking for a variety of Data Scientists to partner with the Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams. You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements PhD with 3 years, MS or MA with 6 years, or BS or BA with 8 years of data science or quantitative modeling experience Proficiency in SQL and a computing language such as Python or R Experience in working with cross-functional teams to deliver results Ability to communicate results clearly and a focus on driving impact A demonstrated ability to manage and deliver on multiple projects with a high attention to detail Strong business acumen and experience in synthesizing complex analyses into actionable recommendations Proficiency with AI tools to accelerate model development, analysis, and coding Preferred qualifications Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation Experience deploying models in production and adjusting model thresholds to improve performance Experience designing, running, and analyzing complex experiments or leveraging causal inference designs A builder's mindset with a willingness to question assumptions and conventional wisdom Experience with distributed tools such as Spark, Hadoop, etc. A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale. This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet. Key responsibilities Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale Own end-to-end performance of our largest RL runs, from research code down to the hardware Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause Minimum qualifications Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack Preferred qualifications Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise Experience developing RL algorithms for language models Experience with scaling laws or other quantitative models of training efficiency Experience designing or modifying transformer architectures beyond standard configurations Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices Experience with C++ or Rust Representative projects Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's business metrics change quickly. New models, products and pricing land every few weeks, and each one moves the numbers in ways leadership needs to understand and act on quickly. The Finance Analytics & Business Intelligence team is hiring a senior individual contributor to own how finance leadership reviews key business metrics. You'll run the weekly company metrics update and forum, explain what is driving revenue and usage across products, customers and models, and turn open-ended business questions into clear, data-driven answers. You'll also build what makes those answers repeatable and scalable: governed metric definitions, executive dashboards, and Claude-powered workflows for recurring analysis. The work is technical, fast-moving and highly visible, and it spans Finance, Product, Go-to-Market, Data Science and Data Engineering. In this role, you will: Own the weekly company metrics update and forum: shape the story the numbers tell, evolve the structure and content as fast as the business changes Define the metrics that matter: help establish the headline KPIs leadership uses to track the business, and the driver views beneath them. Surface macro trends: explain what is driving growth across products, customers, and channels, and flag leading indicators early Lead deep dives on executive questions: take a broad question from leadership, put a measurement approach around it, and work with partner teams to deliver a clear, defensible answer Track performance against plan: explain where actuals differ from forecast and why it matters Partner across teams: work with finance leadership, product and GTM finance and strategy, data science, and data engineering to agree on how we measure the business, so everyone uses the same numbers. Raise the bar: land narratives in executive forums and up-level the team’s finance analytics practice by example You might be a good fit if you: Have run an executive metrics forum: you've owned a recurring business review for senior leadership, including its structure, content and discussion. Land narratives with executives: your analyses have changed business decisions, and you can simplify for senior leaders without losing rigor Put shape around ambiguity: you’ve personally defined the measurement approach for questions nobody knew how to answer, without waiting for a fully specified ask Stay hands-on at senior scope: you still write the SQL and Python yourself, and you’d rather ship a defensible v1 with honest error bars than wait for perfect data Are inherently curious: you go one level deeper than asked and are energized by how fast models, products, and the market are moving Thrive amid shifting priorities: you juggle multiple fast-moving work streams and stay effective when the plan changes weekly Work fluently with modern tooling: you’re strong at data visualization, use Claude and AI tools as force multipliers in analysis and BI, and can self-serve your own workflows across SQL, Python, dbt, and a cloud warehouse Strong candidates may also have: Significant experience in finance analytics, business analytics or data science, including direct partnership with senior leadership Revenue or growth analytics experience at a usage-based business (cloud, API or marketplace) Ownership of a company or executive business review Experience designing evals or benchmarks for AI models or products Fluency in the LLM model and product landscape Experience with a semantic or metrics layer Dimensional modeling and warehouse design experience (grain, SCDs, point-in-time correctness) Cloud platform experience (AWS, GCP) with orchestration, CI/CD for data, and testing/observability The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $270,000 — $345,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background. What you'll do We're looking for a variety of Data Scientists to partner with the Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams. You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience Proficiency in SQL and a computing language such as Python or R Experience in working with cross-functional teams to deliver results Ability to communicate results clearly and a focus on driving impact A demonstrated ability to manage and deliver on multiple projects with a high attention to detail Strong business acumen and experience in synthesizing complex analyses into actionable recommendations Proficiency with AI tools to accelerate model development, analysis, and coding Preferred qualifications Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation Experience deploying models in production and adjusting model thresholds to improve performance Experience designing, running, and analyzing complex experiments or leveraging causal inference designs A builder's mindset with a willingness to question assumptions and conventional wisdom Experience with distributed tools such as Spark, Hadoop, etc. A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)
Who we are About Stripe Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. What you’ll do Responsibilities Scope, design, build, and maintain APIs, services, and large-scale systems that reliably and efficiently handle billions of money movement requests; Design the next generation of Stripe products to meet the high growth needs of our company and customers; Analyze product requirements, user needs and feedback and translate them into technical specifications and determine the feasibility of design, taking into account cost and time constraints; Review API launches and library shapes for other teams to ensure that backwards and forward compatibility, coherent patterns, and extensibility meet our quality standards; Act as the team’s representative during technical meetings and understand how their systems interact with the broader landscape within the company; Mentor early-career engineers to help them grow and spin up into independent engineers; and Construct core engineering tenets and design principles for their team’s projects. Who you are Minimum requirements Master’s degree or foreign equivalent in Statistics, Data Analytics, Engineering or a related field, plus 5 years of related work experience in data science or quantitative modeling. In the alternative, the employer will accept a Bachelor’s degree or foreign equivalent in Statistics, Data Analytics, Engineering or a related field, plus 7 years of related work experience in data science or quantitative modeling, of which at least 5 years must be post-bachelor’s progressive related work experience. Must also have 4 years’ experience in each of the following: SQL; Programming languages including Python or R; Developing models and tuning thresholds to drive business outcomes and deployment of the same model in production; Translating complex data analysis into actionable business outcomes; Writing or contributing to product strategy, roadmap, or technical design documents; Must have 3 years of experience in: Designing and analyzing production experiments to drive strategic and technical outcomes. Salary: $193,232 - 288,000/yr This salary range represents the base salary range for the role and any sales commissions/sales bonuses targets, if applicable, would be in addition to the base salary. 40 hrs/week 50% Telecommuting permitted Multiple Positions Available. Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. WA53 #LI-DNI
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background. What you’ll do We’re looking for a Data Scientist to partner with our Local Payment Methods (LPM) engineering and product teams. You’ll play a key role in understanding, growing, and optimising our LPM business, leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics. Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements PhD, MSc or MA with 2 years, or BS or BA with 3 years of data science or quantitative modeling experience Proficiency in SQL and a computing language such as Python or R Experience in working with cross-functional teams to deliver results Ability to communicate results clearly and a focus on driving impact A demonstrated ability to manage and deliver on multiple projects with a high attention to detail Strong business acumen and experience in synthesizing complex analyses into actionable recommendations Proficiency with AI tools to accelerate model development, analysis, and coding Preferred qualifications Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation Experience deploying models in production and adjusting model thresholds to improve performance Experience designing, running, and analyzing complex experiments or leveraging causal inference designs A builder's mindset with a willingness to question assumptions and conventional wisdom Experience with distributed tools such as Spark, Hadoop, etc. A PhD or MSc in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team The Data Science and Analytics organization at Stripe partners with teams across the company to drive rigorous, data-informed decision-making at scale. Within this org, the Verifications and Greater China data teams deliver critical analytical and data science work—from identity verification and risk modeling to market-specific growth insights—that directly shapes Stripe's ability to serve users safely and expand into new markets. Today, the team comprises individual contributors distributed across Singapore and India, supporting two high-impact pillars. We're looking for a founding Data Science Manager based in Bengaluru to build and lead this growing regional footprint from the ground up. What you'll do This is a rare 0 → 1 leadership role with a dual mandate. Pillar 1—Direct Team Leadership • Manage a team of Data Scientists and Data Analysts (currently 4 individual contributors across India and Singapore) spanning the Verifications and Greater China workstreams. • Own roadmap prioritization, execution quality, and stakeholder alignment for both workstreams. • Drive hiring for open and future roles in India, building a high-caliber data team in a competitive talent market. • Foster individual contributor growth through real-time coaching, mentorship, career development, and performance management. Pillar 2—Regional Data Craft Lead (India Office) • Serve as the founding data craft leader for Stripe's India office. Set quality standards, establish community rituals (knowledge sharing, peer reviews, office hours), and cultivate a strong local data culture. • Act as the go-to point of contact for data craft standards, tooling, and best practices for co-located analysts, even those outside your direct reporting line. • Partner with managers and leads across the broader Data org to ensure consistency in methodology, tooling, and quality bar. • Support onboarding and integration of new data hires in the Bengaluru office. • Over time, this role has the potential to evolve into a Center of Excellence (COE) model—becoming the single point of data leadership in India across multiple product pillars (e.g., Payments, Growth, Marketing), not just Risk. Responsibilities • Build, manage, and develop a high-performing, geographically distributed data team. • Define and drive the data roadmap in close partnership with product, engineering, and business stakeholders—ensuring analytical work is tightly coupled to business outcomes. • Establish and raise the bar on analytical rigor, experimentation frameworks, and data science best practices across the team. • Recruit and retain skilled data talent, crafting a compelling hiring narrative anchored in local leadership and craft excellence. • Champion a culture of technical excellence, intellectual curiosity, and operational discipline. • Collaborate with cross-functional partners and other data leaders globally to align priorities, share learnings, and maintain org-wide consistency. • Communicate insights, recommendations, and team progress clearly to senior leadership. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements • 10+ years of experience in data science, analytics, or a related quantitative field, with 3+ years in a people management role leading data scientists or analysts • Strong technical foundation in SQL, Python or R, statistical modeling, and experimentation design • Demonstrated ability to translate ambiguous business problems into structured analytical frameworks and actionable insights • Experience managing and developing individual contributor talent across multiple levels, including coaching, career pathing, and performance management • Excellent communication and stakeholder management skills—able to influence without authority across functions and time zones along with proven ability to drive alignment and execution across distributed, cross-functional teams Preferred qualifications • Advanced degree (M.S. or Ph.D.) in a quantitative discipline such as Statistics, Economics, Computer Science, Mathematics, or a related field • Experience working in the payments, fintech, or financial services industry • Prior experience building and scaling data teams in a high-growth environment—particularly standing up 0 → 1 functions or teams • Track record of being a builder who has personally architected the rituals, standards, hiring bar, and craft culture for a data team from the ground up • Familiarity with risk, verifications, or compliance-related data domains • Experience operating across Asia-Pacific markets and navigating the nuances of multi-region team management • Passion for developing others and creating environments where individual contributors do the best work of their careers
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Finance and Strategy Data Science builds the forecasting models, data infrastructure, and analytics tools at the core of how Stripe measures and plans its business. The team owns everything from hierarchical time series and agentic forecasting tools that predict payment volumes and revenue margins, to the governed metrics platform that feeds company-wide dashboards and executive reporting. We partner closely with Finance and Strategy, GTM, and Product stakeholders to directly inform financial decisions across Stripe's entire business. The team combines technical depth, strategic thinking, and executive partnership that develops both technical and business expertise. What you’ll do Data Science Managers at Stripe are responsible for the success of their team. You'll be deeply involved in the modeling and design processes as well as coaching, mentoring, and leading the team. You'll have a deep understanding of how to drive efficient data science teams and you'll have a strong user-focus. You'll be working with data scientists, analysts, and engineers on creating technical solutions and communicating effectively across teams and senior leadership. Responsibilities Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data-driven. Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing. Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe. Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers. Recruit and onboard great data scientists, in collaboration with Stripe's recruiting team. Contribute to broad data science initiatives as a member of Stripe's data science management team. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements A PhD, MS, or BS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering) You have at least 3 years of direct management experience leading data science or ML teams, and 10 years of overall data science experience. You've demonstrated expertise in designing metrics and guiding business decisions with data. You have technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions. You've managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems. You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs. You have clear and persuasive communication skills in writing and in speech. You thrive on a high level of autonomy and responsibility. You foster a healthy, inclusive, challenging, and supportive work environment. Preferred qualifications You're comfortable working with geographically distributed teams. Expertise in time series forecasting, predictive modeling, or optimization Expertise in data design and building scalable data architectures
Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust. What you'll do We're looking for a Data Scientist to join the Fraud Data Science team. In this role, you'll build and improve the models that power Stripe's fraud detection and loss management systems. You'll work closely with Fraud Engineering and Risk Operations to move models from research to production, and you'll use data to surface insights that shape fraud strategy across the business. Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk problems in global payments. Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements PhD with 1-3 years, MS or MA with 2-6 years, or BS or BA with 4-8 years of data science or quantitative modeling experience Experience with Fraud, Risk or Financial Crimes Proficiency in SQL and a computing language such as Python or R Experience in working with cross-functional teams to deliver results Ability to communicate results clearly and a focus on driving impact A demonstrated ability to manage and deliver on multiple projects with a high attention to detail Strong business acumen and experience in synthesizing complex analyses into actionable recommendations Proficiency with AI tools to accelerate model development, analysis, and coding Preferred qualifications Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, causal inference, and experimentation Experience deploying models in production and adjusting model thresholds to improve performance Experience designing, running, and analyzing complex experiments or leveraging causal inference designs A builder's mindset with a willingness to question assumptions and conventional wisdom Experience with distributed tools such as Spark, Hadoop, etc. A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Multi-Agent systems are becoming an increasingly important part of how AI is deployed, whether via fast small-model subagents inside a product, or large groups of agents solving very large problems . Training Claude to be maximally effective and safe within large groups is a challenging new area of reinforcement learning, and represents a new axis for scaling test time compute. We are looking for researchers who have experience training multi-agent systems at the largest scale and an appreciation for the incentives and mechanism design that come into play. Responsibilities: Help create and optimize environments and data for model training that maximize Claude’s performance or ease of use on agentic tasks Ideate, develop, and compare the performance of different agent harness configurations (eg memory, context management, communication architectures for agents) Design and implement rigorous quantitative benchmarks for large scale agentic tasks Work with our product org to find solutions to our most vexing challenges in applying agents to our products You may be a good fit if you: Have experience with large-scale RL on language models Have experience training multi-agent systems Enjoy going deeply into the roots of a problem and understanding its foundations, rather than its surface. Have good communication skills and an interest in working with other researchers on difficult tasks Have a passion for making powerful technology safe and societally beneficial Are excited for a mission-driven org with fast-paced, impactful work Representative projects: Design and build reinforcement learning environments to train groups of Claudes how to solve problems together efficiently Design and build agent affordances that unlock new capabilities and scales of agents, while keeping the Bitter Lesson in mind Design and build a novel eval that measures how large teams of agents interact in groups to solve problems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. As a founding member of Life Sciences, you'll work in a high-impact group that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science. Key responsibilities Design, execute, and iterate on the experimental programs at the core of the team's research: molecular biology, biochemistry, protein and nucleic acid characterization, high-throughput functional screens, and the assay development that makes new questions answerable Partner directly with computational biologists to design experiments that produce high-quality, analysis-ready data, and feed results back fast enough to immediately inform the next round of analysis Generate and prioritize hypotheses by combining your experimental judgment with the literature, curated biological knowledge bases, and the team's computational predictions Use Claude and our internal agent frameworks heavily in your own work — for experimental planning, protocol development, and data interpretation — and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases Minimum qualifications Have a Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology) or a related field Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems Have basic proficiency in Python and are familiar with ML development practices Preferred qualifications Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments Can work independently while maintaining strong collaboration with cross-functional teams Are results-oriented, with a bias towards flexibility and impact Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration Published research or practical experience in scientific AI applications Familiarity with modern machine learning techniques and model training methodologies Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for human health and scientific understanding. If you're excited about using your expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 — $320,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma’s Data Science team partners across product, engineering, design, marketing, sales, and operations to help Figma make better decisions with data. As a PhD Data Science Intern, you’ll bring rigorous research training to some of Figma’s most important and open-ended questions, from researching emerging product and user behaviors and Figma’s broader ecosystem to developing new measurement methodologies, applying causal inference or machine learning, and building analytical systems. You’ll own a data science project end-to-end: from framing the question and methodology to translating findings into insights that influence Figma’s products, strategy, or data science practices; with the potential to continue toward publication after the internship. This internship will be based out of our San Francisco or New York hub. What you’ll do at Figma: Partner with cross-functional teams to turn ambiguous product, platform, or business questions into well-defined data science problems Analyze user, product, or business data to uncover insights and recommend actions Design and evaluate experiments, metrics, statistical or machine learning models, and analytical frameworks Communicate assumptions, tradeoffs, limitations, and recommendations clearly to technical and non-technical partners Own a focused internship project end-to-end, from problem framing and technical execution through final recommendations, with potential to continue toward publication after the internship We’d love to hear from you if you have: Currently pursuing a PhD in Data Science, Statistics, Computer Science, Human-Computer Interaction (HCI), Economics, Operations Research, Physics, Applied Mathematics, or a related quantitative field; with demonstrated experience conducting independent research (e.g. thesis or equivalent research milestone) Demonstrated fluency in one or more research methodologies or technical areas, such as statistics, experimentation, machine learning, causal inference, econometrics, optimization, user research methods, or AI/LLMs Experience using a scripting language such as Python or R, as well as proficiency with SQL, for analysis, modeling, or work with complex or large-scale datasets through research, internships, or applied projects Ability to explain technical concepts clearly and connect analysis to decisions, recommendations, or product/business impact A curious, rigorous, and self-starting mindset, with the ability to thrive in ambiguous, fast-moving environments and translate research into real-world impact While it's not required, it's an added plus if you also have: Prior industry or applied research experience with experimental design, causal inference, forecasting, product measurement, user research, or applied machine learning to influence product, business, platform, or stakeholder decisions Publications or research relevant to applied data science Experience or interest in AI product measurement, LLM analytics/evaluation, recommendation systems, search, personalization, or evaluating AI-powered features At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure This internship role is based in either Figma’s San Francisco or New York hub offices, and has the hourly base pay rate stated below. Figma also offers interns a housing stipend and travel reimbursement. Figma’s compensation and benefits are subject to change and may be modified in the future. Internship $68 — $68 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice .