Data Scientist, Financial Engineering

About the Team OpenAI’s Financial Engineering (FinEng) team powers how revenue flows through our products—pricing & packaging, checkout, payments, subscriptions, and the financial infrastructure behind them. We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide. About the Role As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our checkout and payments, subscriptions, and pricing & monetization systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will - Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency. - Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible. - Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action. - Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality. You might thrive in this role if you have - 5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments - Fluency in SQL and Python, with a track record designing and interpreting A/B tests and quasi-experiments - Experience building product metrics from scratch and operationalizing them for decision-making - Excellent communication skills with PMs, engineers, risk/finance partners, and executives - Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience) You could be an especially great fit if you have - Payments, checkout, or subscription analytics experience (PSPs, bank rails, disputes/refunds, risk, e-commerce) - Background in offline incrementality methods, uplift modeling, CUPED/causal inference, or counterfactual evaluation - Experience with internationalization/local payments, FX, and pricing & packaging strategy - Comfort building operational analytics (alerting, SLIs/SLOs) and partnering closely with data engineering 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

5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments. Fluency in SQL and Python, with a track record designing and interpreting A/B tests and quasi-experiments. Experience building product metrics from scratch and operationalizing them for decision-making. Excellent communication skills with PMs, engineers, risk/finance partners, and executives. Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience).

Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency. Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible. Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action. Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality.