Data Scientist, Financial Engineering
Owns analytics and experimentation for checkout, payments, subscriptions, and pricing to boost revenue, reduce churn, and scale globally. Requires 5+ years in data science or product analytics with SQL/Python fluency and A/B testing expertise in high-growth/fintech settings.
About the job
Responsibilities
- 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.
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).
Nice-to-Haves
- 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, A/B Testing, Quasi-Experiments, Product Analytics, Experimentation, Causal Inference, Uplift Modeling, Cuped, Dashboards
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