Data Scientist, FX Quantitative Research

About this role

Lead quantitative research for Stripe’s foreign-exchange risk and hedging work. Develop and productionize models and decision systems for measuring and forecasting FX exposure, sizing hedges, and informing Treasury leadership.

About the company

Stripe provides payment infrastructure and financial tools that help businesses accept payments, manage subscriptions, and move money globally. It serves startups through large enterprises with APIs for online and in-person commerce.

The team

Treasury Finance Data Science, partnering with Treasury, the FX hedging team, Finance, and Engineering.

Stack

What you'll do

• Build FX exposure measurement, VaR/Expected Shortfall, stress tests, and scenario analyses
• Forecast FX exposures and multi-currency cash flows, including uncertainty estimates
• Develop hedge optimization frameworks that balance cost, risk, P&L volatility, and liquidity
• Build and productionize statistical and ML systems for trading and risk
• Own reliability, monitoring, alerting, and incident response for risk-critical FX systems
• Communicate decision-ready analyses to Treasury and Finance leadership
• Set methodology and validation standards, document models, and mentor data scientists

What we're looking for

• Relevant data science or quantitative modeling experience: PhD with 1–3 years, MS/MA with 2–6 years, or BS/BA with 4–8 years
• Quantitative experience at a financial institution with direct FX, rates, or market-risk exposure
• Strong statistics, probability, and time-series modeling; knowledge of VaR, Expected Shortfall, stress testing, and FX hedging instruments
• Production-quality Python and SQL; experience taking models or analytical systems into production
• Strong business judgment, communication, stakeholder collaboration, and project-management skills
• Experience using AI tools to accelerate modeling, analysis, or coding
• Able to work hybrid in New York, with 50% in-office attendance

Nice to have

• Quantitative degree in fields such as mathematical finance, statistics, mathematics, physics, computer science, economics, or operations research
• Applied ML in trading or risk
• Corporate treasury or balance-sheet FX experience, including exposure management, hedge programs, liquidity, or funding
• Risk systems experience with code review, testing, monitoring, and SLAs
• Distributed data tools or workflow orchestration experience
• Fintech, payments, or growth-stage internet company experience
• Leadership reporting experience, such as MBRs or QBRs

Benefits

• Equity
• Company bonus
• 401(k) plan
• Medical, dental, and vision benefits
• Wellness stipends