Data Scientist, FX Quantitative Research

Stripe • New York, NY

2d • Hybrid

About The Position

Stripe is hiring a senior quantitative lead for FX within its Treasury Finance Data Science team. Stripe moves funds across more than 135 currencies, creating FX exposure from merchant flows, multi-currency balances, and entity funding. The role will partner with Stripe’s FX team, Treasury, Finance, and Engineering to develop the quantitative foundation for measuring, forecasting, and hedging that exposure.

This is a hands-on role spanning research, modeling, and production systems. The Data Scientist will shape the research agenda, design methodologies, write production-quality code, and deliver systems whose decision-critical outputs support the FX team and Treasury leadership. The work includes FX risk measurement, exposure and cash-flow forecasting, hedge optimization, trading and risk analytics, and reliable production workflows. The role also raises standards for methodology, validation, documentation, and mentoring within Data Science.

Requirements

• PhD with 1–3 years, MS or MA with 2–6 years, or BS or BA with 4–8 years of data science or quantitative modeling experience.
• 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 risk measures such as VaR, Expected Shortfall, and stress testing, and hedging instruments such as forwards, swaps, and options.
• Proficiency in Python and SQL, with the ability to write production-quality code.
• Experience taking models or analytical systems from research into production.
• Business acumen and ability to translate 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 at once with strong attention to detail.
• Experience using AI tools to accelerate modeling, analysis, and coding.
• Must be located in New York, NY; the role requires 50% in-office attendance.

Nice To Haves

• Degree 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 to engineering standards, including code review, testing, monitoring, and defined SLAs.
• 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 understanding that model and data quality determine the reliability of downstream decisions.

Responsibilities

• Partner with the FX team to build and evolve FX risk measurement, including exposure aggregation across entities and currencies, VaR and Expected Shortfall, stress testing, and scenario analysis.
• Develop forecasts for FX exposures and multi-currency cash flows, quantifying 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, flow anomaly detection, and execution and cost analytics, and take them from prototype to production.
• Own production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflows.
• Work with Treasury Finance stakeholders to translate business questions into quantitative frameworks, anticipate needs, and help shape FX risk strategy.
• Produce clear analyses and reporting to inform Treasury and Finance leadership decisions, including 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior