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.
Responsibilities
Own checkout & payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency.
Create 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.
Requirements
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)
Qualifications
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
Skills
SQL
Python
Benefits
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.
Create 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.
Pay
$230K - $385K
Schedule
Hybrid work model of 3 days in the office per week
OpenAI
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.