142K–214K CAD / year
Data Scientist, FX Quantitative Research
DataHybrid — Toronto, Canada
Published on 2026-11-02
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About this role
Lead quantitative research for Stripe’s foreign-exchange risk and hedging, developing exposure measurement, cash-flow forecasts, and hedge decision frameworks. Build and operate production models and systems that support Treasury and Finance decisions across Stripe’s global currency flows.
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
PythonSQLSparkAirflowFlyte
What you'll do
- Develop FX exposure measurement, VaR and Expected Shortfall, stress tests, and scenario analysis
- Forecast FX exposures and multi-currency cash flows, including quantifying uncertainty
- Build hedge optimization and decision frameworks that account for cost, risk, P&L volatility, and liquidity
- Develop and productionize statistical and machine-learning systems for trading and risk
- Own decision-critical FX systems, including reliability, monitoring, SLAs, and incident response
- Deliver leadership-ready analysis and reporting, including MBRs and QBRs
- Set methodology, validation, and documentation standards; mentor data scientists
What we're looking for
- 8+ years’ relevant experience with a bachelor’s degree, 6+ with a master’s, or 3+ with a PhD
- 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 skills
- Experience taking models or analytical systems from research into production
- Ability to communicate quantitative findings to senior and non-technical stakeholders
- Experience using AI tools to support modeling, analysis, or coding
Nice to have
- Graduate degree in a quantitative field
- Applied machine learning in trading or risk
- Corporate treasury or balance-sheet FX experience
- Experience with production engineering standards, distributed data tools, and workflow orchestration
- Fintech, payments, or growth-stage internet company experience
- Leadership-level reporting experience