Data Scientist, FX Quantitative Research

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. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

Data Science at Stripe is a vibrant 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: liquidity, funding, counterparty risk, and foreign exchange.

Stripe moves funds across 135+ currencies every day. That creates FX exposure from merchant flows, multi-currency balances, and entity funding. In this role, you'll be a thought partner and builder alongside Stripe's FX team. Together you'll develop the quantitative foundation for how Stripe measures, forecasts, and hedges FX exposure.

You'll be the senior quantitative lead for FX within Treasury Finance Data Science. You'll co-build with Treasury, the FX hedging team, Finance, and Engineering: shaping the research agenda, turning ideas into rigorous models, and building the systems that put those models to work. Data Science owns these systems. They produce the decision-critical outputs and analyses that the FX team and Treasury leadership rely on. This is a hands-on role. You'll design the methodology, write the code, and ship it into production.

Responsibilities

• Partner with the FX team to build and evolve FX risk measurement: exposure aggregation across entities and currencies, VaR / Expected Shortfall, stress testing, and scenario analysis
• Develop forecasting models for FX exposures and multi-currency cash flows. Quantify forecast uncertainty so it can inform hedge sizing and timing.
• Co-design hedge optimization and decision frameworks with the FX team that weigh hedge cost, residual risk, P&L volatility, and liquidity constraints
• Build ML and statistical systems for trading and risk (signal research, anomaly detection on flows, execution and cost analytics), and take them from prototype to production
• Own the production systems that generate decision-critical FX outputs, partnering with Engineering on reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflows
• Partner deeply with Treasury Finance stakeholders to turn business questions into quantitative frameworks. Anticipate needs and help shape FX risk strategy rather than reacting to requests.
• Produce clear, trusted analyses and reporting that inform Treasury and Finance leadership decisions, including leadership-level reporting such as MBRs and QBRs
• Use AI tools (code assistants, LLM-based agents) to speed up modeling, analysis, and automation, while maintaining rigor, validation, and auditability
• Raise the technical bar for FX quantitative work within Data Science: methodology standards, model validation, documentation, and mentoring other data scientists

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Location Requirement

• New York, NY required (50% in office - hybrid)

Minimum 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 (VaR, Expected Shortfall, stress testing) and hedging instruments (forwards, swaps, options)
• Proficiency in Python and SQL. You write production-quality code, not just 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 clearly communicate results 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 speed up modeling, analysis, and coding

Preferred qualifications

• Degree in a quantitative field (e.g., Quantitative / Mathematical Finance, Financial Engineering, Statistics, Mathematics, Physics, Computer Science, Economics, Operations Research)
• Applied machine learning experience in trading or risk (e.g., forecasting, execution analytics, signal research, anomaly detection)
• Experience with corporate treasury or balance-sheet FX: multi-entity exposure management, hedge programs, and treasury concepts such as liquidity, funding, and cash positioning
• Experience building risk or decision systems with engineering standards: code review, testing, monitoring, and defined SLAs
• Experience with distributed data tools (e.g., Spark) and workflow orchestration (e.g., Airflow, 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, with a 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 every downstream decision