Data Scientist, FX Quantitative Research

Details

Who We Are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Stripe's mission is to increase the GDP of the internet.

About the Team

Data Science at Stripe is a community where data scientists and data analysts learn and grow together. The Treasury Finance Data Science team builds the models, risk frameworks, and decision tools behind how Stripe manages money at global scale, including liquidity, funding, counterparty risk, and foreign exchange.

Stripe moves funds across more than 135 currencies every day, creating FX exposure from merchant flows, multi-currency balances, and entity funding. In this role, you will work alongside Stripe's FX team to develop the quantitative foundation for measuring, forecasting, and hedging FX exposure.

Responsibilities

• Serve as the senior quantitative lead for FX within Treasury Finance Data Science.
• Partner with Treasury, the FX hedging team, Finance, and Engineering to shape the research agenda, develop rigorous models, and build production systems.
• Build and evolve FX risk measurement capabilities, including exposure aggregation across entities and currencies, Value at Risk (VaR), Expected Shortfall, stress testing, and scenario analysis.
• Develop forecasting models for FX exposures and multi-currency cash flows, and quantify forecast uncertainty to inform hedge sizing and timing.
• Co-design hedge optimization and decision frameworks that weigh hedge cost, residual risk, P&L volatility, and liquidity constraints.
• Build machine learning and statistical systems for trading and risk, including signal research, anomaly detection on flows, and execution and cost analytics, taking them from prototype to production.
• Own production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, service-level agreements, monitoring, alerting, and incident response for risk-critical workflows.
• Partner with Treasury Finance stakeholders to turn business questions into quantitative frameworks and help shape FX risk strategy.
• Produce clear, trusted analyses and reporting for Treasury and Finance leadership, including leadership-level reporting such as MBRs and QBRs.
• Use AI tools, including code assistants and LLM-based agents, to accelerate modeling, analysis, and automation while maintaining rigor, validation, and auditability.
• Raise the technical bar for FX quantitative work through methodology standards, model validation, documentation, and mentoring other data scientists.

Requirements

Minimum 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 quantitative experience at a bank, asset manager, hedge fund, trading firm, or similar financial institution, with direct exposure to FX, rates, or market risk.
• Strong foundation in statistics, probability, and time-series modeling, plus working knowledge of VaR, Expected Shortfall, stress testing, and hedging instruments such as forwards, swaps, and options.
• Proficiency in Python and SQL, with the ability to write production-quality code rather than only research scripts.
• Experience taking models or analytical systems from research into production.
• Business acumen and the ability to turn complex analyses into clear, decision-ready insights.
• Ability to communicate results clearly and drive impact with cross-functional partners, including senior non-technical stakeholders.
• Ability to manage multiple projects simultaneously with strong attention to detail.
• Experience using AI tools to accelerate modeling, analysis, and coding.

Preferred Qualifications

• MS or PhD in a quantitative field such as quantitative or mathematical finance, financial engineering, statistics, mathematics, physics, computer science, economics, or operations research.
• Applied machine learning experience in trading or risk, such as forecasting, execution analytics, signal research, or anomaly detection.
• Experience with corporate treasury or balance-sheet FX, including multi-entity exposure management, hedge programs, liquidity, funding, and cash positioning.
• Experience building risk or decision systems using engineering standards such as code review, testing, monitoring, and defined service-level agreements.
• Experience with distributed data tools such as Spark and workflow orchestration tools such as Airflow or Flyte.
• Prior experience at a growth-stage internet, fintech, or payments company.
• Experience creating leadership-level reporting such as QBRs and MBRs.
• A builder's mindset and willingness to question assumptions and conventional wisdom.
• Interest in how data products enable automated and agentic workflows, and an understanding that model and data quality determine the reliability of downstream decisions.