Quantitative Researcher / Developer (Data Science) - Treasury FX

Quantitative Researcher/Developer (Data Science) - Treasury FX We are seeking a quantitative researcher or quantitative developer to join our Treasury FX Data Science team. You will help build and operate the models and production systems behind FX pricing, risk and trading. Your work will have a direct impact on Wise’s mission and millions of our customers. About

the Role

You'll join the Treasury FX Data Science team, helping own the quantitative models and production infrastructure that power how Wise manages FX risk across USD 250bn+ in annual FX volume - serving everyone from retail customers sending money abroad to tier-1 global investment banks via Wise Platform. The wider Treasury FX team includes quants, traders, analysts, product managers and engineers working together to price, hedge, manage and scale FX operations within Wise in real time. Within that, the Data Science team owns a Python-first, production-grade quant platform which provides multi-instrument pricing, product modelling/monitoring, risk analytics and trading strategies.

We’re hiring for two complementary focus areas

• Quantitative Researcher: Bring deeper expertise in quantitative modelling to develop and improve pricing, forecasting, risk analytics and hedging methodology with rigorous backtesting and stakeholder engagement
• Quantitative Developer: Bring deeper expertise in quantitative engineering to develop and improve production services, shared quant libraries and engineering reliability. You are expected to

Your focus will reflect your strengths, with opportunities to contribute across both areas and broaden your expertise. We expect depth in one area, with a strong shared foundation in Python, quantitative reasoning and production ownership. What you’ll own

• A primary focus in either production quantitative engineering or applied quantitative modelling
• Python implementation of quantitative work from research or prototype through reliable production use
• Validation, backtesting and monitoring of model and service performance against realised outcomes
• Deployment, incident response, root-cause analysis and continuous improvement with stakeholders
• Shared quant libraries used across multiple services
• CI/CD pipelines, deployments and operational excellence
• Monitoring, alerting, and reliability for real-time pricing and risk systems

Where you’ll grow

• Market data management and onboarding new pipelines..
• Designing new quant infrastructure and systems with the engineering team.
• Backtesting frameworks, model validation and risk modelling alongside the Risk team (VaR, stress testing, scenario analysis).
• Customer behaviour modelling, pricing strategy and product development.
• Collaborating with product teams to translate quantitative insights into customer-facing decisions