Stripe is hiring a Data Scientist in Toronto!
Stripe's Treasury Finance Data Science group is hiring a Data Scientist to act as the senior quantitative lead for foreign-exchange risk. The team is responsible for the models, risk frameworks, and decision tooling that govern how Stripe manages money globally — liquidity, funding, counterparty risk, and FX. Because Stripe processes payments in more than 135 currencies daily, merchant flows, multi-currency balances, and entity-level funding all create FX exposure that must be measured, forecasted, and hedged. This role sits directly alongside the FX team as a thought partner and builder, owning the quantitative foundation behind those decisions. It is a hands-on position rather than a purely advisory one: the successful candidate designs the methodology, writes the code, and carries it into production, then owns the resulting systems and the outputs Treasury and Finance leadership depend on. The team is described as a collaborative community of data scientists and analysts, and the role carries expectations around raising methodological standards, model validation, documentation, and mentoring. Stakeholders span Treasury, the FX hedging team, Finance, and Engineering, with visibility up to leadership-level reporting.
Key responsibilities
- Build and refine FX risk measurement with the FX team: exposure aggregation across entities and currencies, VaR and Expected Shortfall, stress testing, and scenario analysis
- Develop forecasting models for FX exposures and multi-currency cash flows, and quantify forecast uncertainty so it informs hedge sizing and timing
- Co-design hedge optimization and decision frameworks that balance hedge cost, residual risk, P&L volatility, and liquidity constraints
- Research and productionize machine learning and statistical systems for trading and risk, including signal research, flow anomaly detection, and execution/cost analytics
- Own production systems generating decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response
- Translate Treasury Finance business questions into quantitative frameworks, anticipating needs and helping shape FX risk strategy proactively
- Deliver clear, trusted analyses and reporting for Finance and Treasury leadership, including MBRs and QBRs
- Apply AI tooling such as code assistants and LLM-based agents to accelerate modeling and automation while preserving rigor, validation, and auditability
- Elevate the technical bar for FX quant work across Data Science through methodology standards, model validation, documentation, and mentoring
Key requirements
- BS/BA with 8+ years, MS/MA with 6+ years, or PhD with 3+ years of full-time experience in quantitative finance, risk modeling, quantitative research, or data science
- Hands-on quant experience at a bank, asset manager, hedge fund, trading firm, or similar institution, with direct exposure to FX, rates, or market risk
- Strong grounding in statistics, probability, and time-series modeling, plus knowledge of risk measures (VaR, Expected Shortfall, stress testing) and hedging instruments such as forwards, swaps, and options
- Proficiency in Python and SQL, writing production-quality code rather than research scripts
- Demonstrated experience moving models or analytical systems from research into production
- Business acumen and the ability to convert complex analyses into decision-ready insight
- Strong communication and cross-functional influence, including with senior non-technical stakeholders
- Ability to juggle multiple projects with close attention to detail
- Experience using AI tools to speed up modeling, analysis, and coding
- Preferred: MS or PhD in a quantitative field; applied ML in trading or risk; corporate treasury or balance-sheet FX experience; engineering-grade risk systems with code review, testing, monitoring, and SLAs; Spark and orchestration tools such as Airflow or Flyte; fintech or payments background; QBR/MBR reporting experience
- Seniority: Senior
- Work mode: Not specified
- Key skills: Python, SQL, Time-series modeling, VaR, Expected Shortfall, Stress testing, Scenario analysis, FX risk management, Hedging, Machine learning, Forecasting, Spark, Airflow, Flyte, Model validation
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