Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Python @ 6
SQL @ 6
Data Science @ 4
Leadership @ 7
Communication @ 7
Data Engineering @ 4
Mentoring @ 4
Experimentation @ 4
Fraud @ 4
Reporting @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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. The team operates at the intersection of Product, Engineering, Risk, Finance, and Go-to-Market to ensure paying for OpenAI products is seamless, reliable, scalable, and globally optimized.
About the Role
As Manager of Data Science for Financial Engineering, you will lead the measurement, experimentation, and optimization strategy that powers OpenAI’s monetization infrastructure. You will define how we measure and improve checkout, payments, subscriptions, and pricing systems globally—balancing conversion, risk, cost, reliability, and user experience. You will build and lead a team responsible for establishing source-of-truth metrics, scaling experimentation, and driving executive-level revenue insights. This role is both strategic and deeply technical: you will shape long-term financial data architecture while guiding day-to-day experimentation that directly impacts revenue and international scale.
Location & Work Model
This role is based in San Francisco, CA. OpenAI uses a hybrid model (3 days/week in office) and offers relocation support.
Responsibilities
- Own the FinEng measurement strategy: define north-star revenue and monetization metrics across checkout, payments, subscriptions, and pricing; establish guardrails across conversion, fraud/risk, payment latency, cost-to-serve, and reliability; partner with Finance to align product metrics and financial reporting.
- Lead and scale experimentation: build and oversee the experimentation program for in-house checkout and subscription systems; define staged rollouts, guardrails, and offline incrementality methods when online testing is constrained; raise the bar on causal rigor across monetization decisions.
- Build and lead the FinEng DS team: hire, mentor, and grow a team of high-impact data scientists; set the technical direction for experimentation, causal inference, and monetization analytics; create operating rhythms that translate insights into shipping decisions.
- Drive global monetization optimization: lead analytics for international payment method expansion, FX strategy, and pricing localization; reduce involuntary churn through intelligent retry logic, targeted nudges, and payment optimization; develop elasticity frameworks and pricing models that inform packaging and long-term revenue strategy.
- Build durable data infrastructure: partner with FinEng Data Engineering to create source-of-truth datasets and operational visibility; establish SLIs/SLOs, alerting, and proactive monitoring across payment flows; ensure analytics scales with product and geographic expansion.
Requirements
- 7+ years in data science, experimentation, or product analytics, including leadership experience.
- Experience leading monetization, payments, checkout, or subscription analytics.
- Deep fluency in SQL and Python.
- Strong causal inference instincts and experience with experimentation/causal methods.
- Track record of building experimentation platforms or scaling testing programs.
- Experience managing or mentoring high-performing data scientists.
- Strong executive communication skills and ability to influence cross-functional leaders.
Preferred / Nice to Have
- Payments infrastructure or PSP experience (bank rails, disputes, fraud/risk systems).
- Background in offline incrementality, uplift modeling, CUPED, or counterfactual evaluation.
- Experience with global payment methods, FX strategy, and pricing optimization.
- Built operational analytics systems (alerting, SLIs/SLOs, monitoring).
- Partnered closely with Finance or revenue accounting teams.
Benefits
- Base pay range listed for the role; in addition to salary, total compensation includes equity and potential performance-related bonus(es).
- Medical, dental, and vision insurance with employer contributions to Health Savings Accounts.
- Pre-tax accounts (Health FSA, Dependent Care FSA, commuter benefits).
- 401(k) retirement plan with employer match.
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents) and paid medical/caregiver leave.
- Paid time off (flexible PTO for exempt employees; up to 15 days annually for non-exempt), 13+ paid company holidays, and additional coordinated office closures.
- Mental health and wellness support; employer-paid basic life and disability coverage.
- Annual learning and development stipend; daily meals in offices and meal delivery credits as eligible.
- Relocation support for eligible employees; additional taxable fringe benefits (e.g., donation matching, wellness stipends).