Data Scientist, FX Quantitative Research

Description

The company is a financial infrastructure platform for businesses, used by millions of companies to accept payments and manage revenue across 135+ currencies daily.

The Treasury Finance Data Science team builds the models, risk frameworks, and decision tools that manage liquidity, funding, counterparty risk, and foreign exchange at global scale.

Moving funds across currencies creates FX exposure from merchant flows, multi-currency balances, and entity funding. In this role you'll work alongside the FX team as a thought partner and builder, developing the quantitative foundation for how that exposure is measured, forecast, and hedged.

Data Science is a collaborative community of data scientists and data analysts who learn and grow together.

Responsibilities

• Act as the senior quantitative lead for FX within Treasury Finance Data Science, working with Treasury, the FX hedging team, Finance, and Engineering to set the research agenda, turn ideas into rigorous models, and build the systems that run them.
• Design the methodology, write the code, and move it into production, since you'll be hands-on from start to finish.
• Own the systems that produce decision-critical FX outputs, including reliability, SLAs, monitoring, alerting, and incident response for risk-critical workflows, with Engineering.
• Work with the FX team to build and improve FX risk measurement: aggregating exposure across entities and currencies, VaR and Expected Shortfall, stress testing, and scenario analysis.
• Build forecasting models for FX exposures and multi-currency cash flows, and quantify forecast uncertainty to guide hedge sizing and timing.
• Design hedge optimization and decision frameworks with the FX team that account for hedge cost, residual risk, P&L volatility, and liquidity constraints.
• Build ML and statistical systems for trading and risk, such as signal research, flow anomaly detection, and execution and cost analytics, and take them from prototype to production.
• Work closely with Treasury Finance stakeholders to translate business questions into quantitative frameworks, anticipating their needs and helping shape FX risk strategy.
• Produce clear, trusted analyses and reports for Treasury and Finance leaders, including MBRs and QBRs.
• Use AI tools such as code assistants and LLM-based agents to speed up modeling, analysis, and automation, while keeping work validated and auditable.
• Set the technical bar for FX quantitative work: methodology standards, model validation, documentation, and mentoring of other data scientists.

Requirements

• You must meet the minimum requirements to be considered. The preferred qualifications are a bonus and not required, so apply if you meet the minimum.

Conditions

Based in New York, NY, with hybrid work at 50% in office.

Minimum requirements: a PhD with 1-3 years, an MS or MA with 2-6 years, or a 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 institution, with direct exposure to FX, rates, or market risk.

Strong grounding 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 production-quality code rather than only research scripts.

Experience moving models or analytical systems from research into production.

Business judgment and the ability to turn complex analysis into clear, decision-ready insights.

Ability to communicate results and drive impact with cross-functional partners, including senior non-technical stakeholders.

Ability to juggle several projects at once with close attention to detail.

Experience using AI tools to speed up modeling, analysis, and coding.

Preferred qualifications (optional): a degree in a quantitative field such as Quantitative or Mathematical Finance, Financial Engineering, Statistics, Mathematics, Physics, Computer Science, Economics, or Operations Research.

Preferred qualifications (optional): applied machine learning in trading or risk, such as forecasting, execution analytics, signal research, or anomaly detection.

Preferred qualifications (optional): corporate treasury or balance-sheet FX experience, including multi-entity exposure management, hedge programs, and treasury concepts like liquidity, funding, and cash positioning.

Preferred qualifications (optional): experience building risk or decision systems to engineering standards, with code review, testing, monitoring, and defined SLAs.

Preferred qualifications (optional): experience with distributed data tools such as Spark and workflow orchestration tools such as Airflow or Flyte.

Preferred qualifications (optional): prior work at a growth-stage internet, fintech, or payments company.

Preferred qualifications (optional): experience producing leadership-level reporting such as QBRs and MBRs.

Preferred qualifications (optional): a builder's mindset, willing to question assumptions and conventional wisdom.

Preferred qualifications (optional): 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.