Lead Data Scientist - Treasury Markets Quant

Wise
London, UK
14 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
4 years minimum
Working hours
Regular working hours

Tech stack

Databases Software Debugging Python (Programming Language) Monte Carlo Methods Redis Backtesting Snowflake Apache Spark Model Validation Event Driven Architecture Data Lakes Apache Flink
+4 more
Apache Kafka Stream Processing Data Pipelines Microservices

Job description

reliability for real-time pricing and risk systems Shared quant libraries used across multiple services CI/CD pipelines, deployment and operational excellence Incident response and root cause analysis for production issues Where you’ll grow Real-time curve construction (yield curves, FX forwards, vol surfaces) Pricing models for new instruments and products Trading strategy development and optimisation Risk modelling alongside the Risk team (VaR, stress testing, scenario analysis) Backtesting frameworks and model validation Customer behaviour modelling, pricing strategy and product launch support Collaborating with product teams to translate quantitative insights into customer-facing decisions Qualifications What we’re looking for 4+ years building and maintaining production Python systems Strong experience with microservices, databases, and production infrastructure Experience with streaming systems, real-time data pipelines, or event-driven architectures (Kafka, Flink, Redis etc.)

Requirements

Quantitative background - maths, physics, engineering or finance - you can read a model and reason about correctness Experience with testing, monitoring, and debugging complex systems under load A product mindset - you think about who uses your systems and why Clear communicator who can work cross-functionally with other quants, analysts, traders, product managers and engineers It’s a bonus if you are familiar with FX or financial markets experience Term structure modelling, stochastic calculus or Monte Carlo methods Interest rate curve bootstrapping Algorithmic execution experience Data lake or warehouse experience (Snowflake, Iceberg, Spark etc.) We’re people without borders - without judgement or prejudice, too. We want to work with the best people, no matter their background. So if you’re passionate about learning new things and keen to join our mission, you’ll fit right in. Also, qualifications aren’t that important to us. If you’ve got great experience, and you’re great at articulating your thinking, we’d like to hear from you. And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under-represented demographic Additional Information For everyone, everywhere. We’re people building money without borders - without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive. We’re proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers. If you want to find out more about what it’s like to work at Wise visit Wise.Jobs. Keep up to date with life at Wise by following us on LinkedIn and Instagram.

About the company

quantitative infrastructure that powers how Wise manages FX risk across a USD 250bn+ in annual FX volume- serving everyone from retail customers sending money abroad to tier-1 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 the quantitative platform: We run a Python-first, production-grade quant platform: real-time curve construction, multi-instrument pricing, risk analytics, and trading strategy - all built and operated by the same team. Your primary focus is keeping these systems reliable, performant and well-engineered - while thinking deeply about how they serve customers and products. You’ll also contribute to the quantitative models themselves as you grow into the domain. What you’ll own Python microservices that run quantitative models in production Monitoring, alerting, and

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