Stripe
Mid
Drive Stripe's Local Payment Methods business with modeling, causal inference, and experimentation
Stripe is hiring a Data Scientist to partner with its Local Payment Methods (LPM) engineering and product teams, using machine learning, statistical modeling, causal inference, optimization, and experimentation to grow and optimize the LPM business. This is a relatively junior bar for Stripe (2 years post-PhD/MSc or 3 years post-bachelor's) with broad Data Science team scope. Interview should probe core quantitative modeling fundamentals, SQL/Python or R proficiency, and the ability to turn analysis into a clear business recommendation for a specific payments product area.
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What this interview tests
- Statistical modeling and causal inference on payments/LPM business questions
- Experiment design and analysis for product/business decisions
- SQL and Python/R for data science workflows
- Cross-functional delivery with engineering and product teams
- Translating analysis into clear, actionable business recommendations
Common question themes
How would you measure the impact of adding a new local payment method
Design an analysis when you can't run a clean randomized experiment
Walk through a project where you delivered a recommendation to a cross-functional team
What's your approach to prioritizing multiple concurrent analyses
How do you use AI tools to accelerate your modeling or analysis work
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