Browse open Data Scientist positions aggregated from verified tech companies. Optimize your resume for these roles using our free analyzer and resume examples.
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
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 .
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)
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 You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a focus on core systems and cloud platforms. Projects include, but are not limited to: Developing models to predict resource needs as Stripe demand increases; Working closely with engineers to improve the cost and performance of platforms and services; Employing quantitative methods to drive and automate fleet decisions. You will act as a key strategic data partner to the Core Infrastructure organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe can continue to scale with efficiency and dependability as our business rapidly grows. What you'll do As a Data Scientist, your role will involve: Analyzing infrastructure usage, efficiency, and workloads to predict demand and inform capacity planning. Developing models and strategies for efficient compute resource consumption and provisioning. Collaborating with engineers, engineering leadership, and finance teams to ensure Stripe makes the right, data-driven, infrastructure decisions. Providing actionable insights and recommendations to improve infrastructure operations to reduce costs and improve reliability. Utilizing your analytical expertise to influence both technical and financial strategies within Stripe. 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 Seattle, WA or San Francisco, CA (Hybrid: 50% in office) 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. 3-8+ years of experience with a focus on infrastructure, cloud environments, and resource utilization/allocation. Proficiency in SQL and a computing language such as Python or R. Experience in analyzing logs/telemetry, scheduling optimization, or cloud infrastructure engineering. 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. Preferred qualifications Background in deploying data models in production environments and optimizing their performance. Experience in using, deploying on, and analyzing usage data from public cloud providers. Familiarity with distributed computing tools such as Spark and Hadoop. A PhD or MS in a quantitative field like Computer Science & Engineering, Statistics, Mathematics, Operations Research, Industrial Engineering, Management Science, or related disciplines. Strong business acumen with a track record of translating complex data analyses into actionable business recommendations.
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 an intern on our team, you'll work on technical challenges that have real impact on our product and business, and we're looking for interns with strong foundations in coding and statistics and who are clear communicators with both technical and non-technical audiences. This internship will be based out of our San Francisco or New York Hub. What you'll do at Figma: Own a scoped project end to end—from framing the question with Product, Engineering, Design, and Data Science partners through delivering the work—with support from a manager and mentor. Analyze user behavior, product performance, or business data to uncover insights and translate them into recommendations for businessDesign and evaluate experiments and develop metrics that help teams understand the impact of product changes.Build models to understand user behavior and predict growth Build and improve data pipelines and tools that make product data more scalable and accessible Focus areas may include analytics, experimentation, or infrastructure - based on your interests and business needs Some projects you could work on: Analyze how people adopt and use a new or existing Figma product experience, define meaningful success metrics, and turn patterns in engagement and retention into product recommendations. Build and validate a new analytical method or tool that expands how Figma’s Data Science team solves recurring problems. Study marketing or customer-journey data to identify drivers of acquisition, conversion, or retention. Develop and evaluate forecasts or models that help teams anticipate user or business trends and plan more effectively We'd love to hear from you if you have: Experience using SQL and a scripting language such as Python or R to clean, explore, and analyze data through research, projects, or prior work. A solid foundation in statistics and quantitative reasoning, with exposure to areas such as experimentation, forecasting, statistical modeling, causal inference, or data modeling. The ability to structure open-ended questions, validate your analysis, and connect findings to product or business decisions. Curiosity, initiative, and strong communication and collaboration skills with both technical and non-technical partners. 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 $46 — $46 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 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 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 Global Growth teams. You’ll play a key role in designing and shipping experiments, as well as identifying improvement opportunities across stripe.com and the dashboard to help businesses worldwide get started on Stripe. You’ll help us understand, grow, and optimize the self-serve user funnel to ensure a consistently high-quality onboarding experience for users globally. As Data Scientists at Stripe, our mission is to ensure that 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 Bachelors + 8 years or Masters + 6 years or Phd + 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 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)
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 You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a special focus on Stripe’s security. Projects include, but are not limited to: Leverage internal telemetry and logs to understand and design secure and safe access controls to sensitive data; Develop methods to model, quantify, and ultimately de-risk security-related incidents on Stripe data, assets, and networks; Collaborate across the company with engineering, PMs, and others to better understand, measure, and ultimately detect various malicious attack vectors. You will act as a key strategic data partner to the Security organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe keeps and maintains the highest level of safety and security for critical business assets and customer data. What you'll do Responsibilities Provide senior technical direction to data teams on horizontal technical areas, including detection, modeling, metrics, observability, etc. Assume hands-on leadership, especially when helping teams resolve complex problems through iterative execution. Identify broad company problems and opportunities that can be tackled through data science Work with relevant teams to design and build the quantitative outputs and artifacts that deliver outsized value to our users and our business. Provide data-driven guidance to cross-functional partners on strategy for tracking and protecting Stripe assets from external and internal threats. Contribute to the overall strategy, roadmap, and vision of your data science team and organization. Evangelize and inspire best practices across data science. Lead by example to build a culture of craftsmanship and innovation. Provide mentorship to our data science talent to help them grow technically and professionally. 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 data science experience or equivalent combined industry and research experience in a quantitative field. Bachelor’s, Master’s, or Ph.D. in a quantitative field (e.g. Statistics, Mathematics, Economics, Operations Research, Quantitative Marketing, Physical Sciences, Engineering, etc.). Demonstrated experience of leading organization-wide initiatives spanning multiple teams, or leveraging deep domain expertise to influence tech roadmap planning and execution. Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes. Experience creating alignment with stakeholders in ambiguous and complex situations, and leading company-level initiatives. Demonstrated ability to balance execution and velocity with research, statistical depth, and scalable design. Proficiency with AI tools to accelerate model development, analysis, and coding. Experience mentoring and investing in the development of peers. Preferred qualifications Strong preference for experience working with security or security-adjacent teams, and familiarity with contemporary security tools and practices. Experience in the end-to-end development and production implementation of machine learning, statistical, or forecasting frameworks (beyond building model prototypes). Experience developing and deploying metrics and observability frameworks.
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
About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. About the Role As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. In this role, you will: Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions of fairness, and clearly document the assumptions, limitations, and residual risks of each approach. Evaluate end-to-end human-AI decision systems, including model outputs, user behavior, human overrides, escalation pathways, and whether AI assistance changes the quality, consistency, or equity of decisions. Develop evaluation approaches for generative and agentic AI, including test-set design, counterfactual testing, behavioral evaluation, human-rating studies, robustness testing, and analysis of disparate performance across populations and contexts. Investigate the sources of observed disparities, including data representation, label and measurement bias, proxy variables, model design, decision thresholds, workflow design, and differential adoption or usage. Partner with engineering, People Operations, Legal, Privacy, Security, and People Systems teams to recommend and evaluate mitigations such as data improvements, model changes, threshold adjustments, workflow redesign, monitoring controls, and additional human oversight. Build scalable fairness-evaluation infrastructure, including reusable datasets, automated validation pipelines, regression tests, monitoring systems, self-service tools, and standardized reporting. Establish research and documentation standards for fairness test plans, dataset and model documentation, validation reports, limitations, monitoring plans, and decision records. Translate complex findings into concise, decision-ready narratives, helping leaders understand the significance of identified risks, the strength of the evidence, available mitigation options, and remaining uncertainty. You might thrive in this role if you have: Deep expertise in algorithmic fairness, bias measurement, responsible AI, psychometrics, applied statistics, or the evaluation of high-impact decision systems. Exceptional strength in research design, measurement, experimentation, causal inference, and statistical modeling. Hands-on experience applying methods such as subgroup and intersectional analysis, adverse-impact testing, equalized-odds and equal-opportunity analysis, demographic-parity assessment, calibration analysis, counterfactual testing, measurement invariance, reliability analysis, and validation studies. Strong judgment about the limitations of fairness metrics, including the ability to determine which measures are appropriate for a particular decision context rather than applying a single universal definition of fairness. Experience evaluating machine-learning models, generative AI systems, agents, or human-AI workflows using quantitative and qualitative evidence. High proficiency in Python or R and SQL, with experience working across complex, sensitive, and imperfect datasets. Experience building reproducible evaluation pipelines, automated testing frameworks, analytical tools, monitoring systems, or governed research workflows. Ability to distinguish statistical disparities from their potential causes and to communicate findings without overstating certainty or making unsupported causal or legal conclusions. Ability to work effectively with technical, operational, legal, privacy, and executive stakeholders and influence consequential decisions through evidence and sound judgment. Deep curiosity, intellectual humility, strong attention to detail, and a commitment to developing AI systems and organizational processes that work well for people across different backgrounds and circumstances. Preferred Qualifications Experience conducting fairness assessments, algorithmic audits, model-risk reviews, adverse-impact analyses, or validation studies in employment or another high-impact domain. Familiarity with fairness and model-evaluation tools such as Fairlearn, AI Fairness 360, responsible-AI evaluation frameworks, explainability methods, or comparable internal tooling. Experience evaluating large language models, generative AI systems, safety classifiers, or agentic workflows, including behavioral testing and human evaluation. Experience with employment selection, talent assessment, psychometrics, organizational research, or the validation of hiring, performance, promotion, or workforce decisions. Familiarity with responsible-AI frameworks and emerging requirements related to automated employment decision systems, algorithmic auditing, data privacy, and AI governance. Experience creating model cards, dataset documentation, fairness scorecards, audit reports, monitoring plans, or other review artifacts for high-impact systems. Advanced degree in Quantitative Psychology, Computer Science, Statistics, Economics, Data Science, Behavioral Science, or a related quantitative field; PhD preferred but not required. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About The Role: You will apply your expertise in statistical inference and experimentation to understand the impact of product changes and drive decision-making. You will explore ambiguous areas and conduct analysis to identify opportunities for the Growth org, helping shape the strategy and roadmap. You'll partner closely with product, engineering, and marketing leaders at every step of the development process, driving forward data science and analytics work. You'll work with your teams to determine north star and operational metrics, and build the foundational datasets and dashboards to monitor progress and make key decisions. You'll influence the broader company by communicating your findings and driving change in our product and business. (Insights are useful. Impact is even better!) Skills You'll Need to Bring: You are comfortable with ambiguity and motivated by business results. You have expertise in SQL and at least one scripting language (ideally Python or R). You know how to use statistical inference and experimentation to drive actionable recommendations. You are comfortable transforming raw data to build your own data sets if the measure you need doesn't exist yet. You have a bias for using the right tools to get a job done with maximum efficiency. You have experience making tradeoffs between speed and accuracy. Nice to Haves: You have worked at a fast-growing start-up. You have experience at a B2B SaaS company. You have a track record of acting as a thought partner to product, engineering, and marketing leaders, driving strategic decisions on these teams using data. You have experience building predictive models, and you know how to evaluate their effectiveness. We hire talented and passionate people from a variety of backgrounds because we want our global employee base to represent the wide diversity of our customers. If you’re excited about a role but your past experience doesn’t align perfectly with every bullet point listed in the job description, we still encourage you to apply. If you’re a builder at heart, share our company values, and enthusiastic about making software toolmaking ubiquitous, we want to hear from you. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know. Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $145,000 – $233,000 per year. By clicking “Submit Application”, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion’s Global Recruiting Privacy Policy . #LI-Onsite A Note on AI You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on. Equal Opportunity & Accommodations We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.
About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make better, evidence-based talent decisions. About the Role As a People Research Scientist, you will bring deep expertise in research design, measurement, experimentation, and applied data science to OpenAI’s most important People programs. You will design studies, evaluate people processes, and help leaders better empower employees, strengthen organizational systems, and deliver exceptional employee experiences. This is a high-ownership individual contributor role combining hands-on research, methodological leadership, and scalable people science capabilities. We’re looking for an experienced researcher who can turn ambiguous People questions into rigorous designs, validated insights, and actionable recommendations. This role is based in San Francisco, CA or Mountain View, CA, with occasional travel to our San Francisco office. What You’ll Do: Design rigorous research and evaluation strategies for recruiting, organizational health, manager effectiveness, employee experience, and talent outcomes. Apply advanced statistical modeling, machine learning, and research methods to inform program design, evaluate effectiveness, and quantify business impact. Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, establish documentation standards, and ensure research datasets are governed, reproducible, and privacy-preserving. Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable research datasets and analytical pipelines. Develop research playbooks that establish rigorous standards for study design, measurement, validation, and documentation, enabling high-quality, repeatable, and scalable research across the organization. Communicate findings through concise, executive-ready narratives. What We’re Looking For Deep curiosity, strong attention to detail, and passion for solving ambiguous and complex problems with creativity. Exceptional strength in research design, experimentation, measurement, causal inference, and statistical modeling, including hands-on experience with psychometrics, survey methodology, structural equation modeling, multilevel modeling, randomized controlled experiments, A/B testing, quasi-experimental design, validation studies, and machine learning evaluation. High proficiency in R or Python and SQL, with experience working across complex, messy datasets. Experience building measurement systems, research programs, data products, reusable analytics frameworks, self-service tools, and governed analytical workflows. Ability to communicate complex methods and tradeoffs clearly to senior leaders, technical partners, and non-technical audiences. Sound judgment in handling sensitive employee data, including privacy, fairness, bias, and responsible research practices. Preferred qualifications Experience evaluating AI-assisted workflows, algorithmic systems, and human-AI decision processes in operational contexts, including familiarity with model evaluation methods. Advanced degree in Industrial-Organizational Psychology, Organizational Behavior, Quantitative Psychology, Behavioral Economics, Statistics, Economics, Data Science, or a related field. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement . Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form . No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link . OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
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 As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. Responsibilities: Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls – and analyze the results in order make actionable recommendations Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes Present complex technical analyses and recommendations to both technical and non-technical stakeholders Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow You may be a good fit if you have: 7+ years of experience in data science or analytics roles Deep expertise with Python, SQL, and data visualization tools Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments Highly effective written communication and presentation skills A track record of translating complex data into clear, actionable insights for both technical and business stakeholders A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress A passion for the company’s mission of building helpful, honest, and harmless AI Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem 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: $285,000 — $380,000 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.