Data Scientist, FX Quantitative Research

Description

The company is a financial infrastructure platform for businesses, used by millions of companies to accept payments, grow revenue and open new business opportunities. 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.

The company moves funds across 135+ currencies every day, which creates FX exposure from merchant flows, multi-currency balances and entity funding. You will act as a thought partner and builder alongside the FX team, developing the quantitative foundation for how FX exposure is measured, forecast and hedged.

Responsibilities

• Serve as the senior quantitative lead for FX within Treasury Finance Data Science, co-building with Treasury, the FX hedging team, Finance and Engineering to set the research agenda, turn ideas into rigorous models and build the systems that use them.
• Design the methodology, write the code and deploy it to production. Data Science owns the systems that produce decision-critical outputs and analyses relied on by the FX team and Treasury leadership.
• With the FX team, build and extend FX risk measurement: exposure aggregation 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.
• With the FX team, design hedge optimization and decision frameworks that weigh hedge cost, residual risk, P&L volatility and liquidity constraints.
• Build ML and statistical systems for trading and risk, including signal research, flow anomaly detection and execution and cost analytics, taking them from prototype to production.
• Own the production systems that generate decision-critical FX outputs. Work with Engineering on reliability, SLAs, monitoring, alerting and incident response for risk-critical workflows.
• Work closely with Treasury Finance stakeholders to convert business questions into quantitative frameworks, anticipating needs and helping shape FX risk strategy.
• Produce clear, trusted analyses and reporting for Treasury and Finance leadership, 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.
• Raise the technical bar for FX quantitative work, setting methodology standards, validating models, improving documentation and mentoring other data scientists.

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 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 acumen and the ability to turn complex analyses into clear, decision-ready insights.
• Ability to communicate results and drive impact with cross-functional partners, including 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, not required: MS or PhD in a quantitative field such as Quantitative or Mathematical Finance, Financial Engineering, Statistics, Mathematics, Physics, Computer Science, Economics or Operations Research.
• Preferred, not required: applied machine learning in trading or risk, such as forecasting, execution analytics, signal research or anomaly detection.
• Preferred, not required: corporate treasury or balance-sheet FX, including multi-entity exposure management, hedge programs and treasury concepts like liquidity, funding and cash positioning.
• Preferred, not required: building risk or decision systems with engineering standards such as code review, testing, monitoring and defined SLAs.
• Preferred, not required: experience with distributed data tools such as Spark and workflow orchestration tools such as Airflow or Flyte.
• Preferred, not required: prior work at a growth-stage internet, fintech or payments company.
• Preferred, not required: experience producing leadership-level reporting such as QBRs and MBRs.
• Preferred, not required: a builder's mindset, willing to question assumptions and conventional wisdom.
• Preferred, not required: interest in how data products enable automated and agentic workflows, and an understanding that model and data quality determine how reliable every downstream decision is.

Conditions

The role is based within Treasury Finance Data Science and works alongside the FX team.

Equal opportunity applies to all candidates.