Senior Data Scientist​/ML Engineer - Financial Crime

Help build ML systems that make financial crime harder to hide

Financial crime is constantly changing. New patterns and behaviours emerge all the time, and the data we work with is complex. Our work helps make financial activity safer and more trustworthy for merchants, customers, and the company.

As a Senior Data Science/ML Engineer in the Risk AI Engineering Squad, you will build the production systems that turn machine learning into reliable, explainable transaction-monitoring capabilities. You will work across the full model lifecycle: understanding financial-crime typologies, exploring data, engineering features, training and validating models, deploying them at scale, and monitoring their performance over time.

This role is designed for someone who is strongest on the engineering side of machine learning and wants to keep growing their data-science depth. You do not need to be a traditional data scientist or ML Engineer. We’re looking for someone who enjoys working across both disciplines: building robust, production-ready software while staying close to the data, models, and decisions those systems support.

You will join a cross-functional team within the Risk & Compliance tribe, working closely with AML and Fraud Operations, investigators, Product, and Engineering. Together, we build data products and ML solutions that help Risk teams work smarter, faster, and more effectively — while keeping our controls robust, auditable, and compliant across products and markets.

We actively welcome applications from women and people from underrepresented backgrounds. Diverse perspectives make our team stronger and our systems more robust. If you're motivated by technical depth, real-world impact, and the challenge of making ML work reliably in a high-stakes environment, this role is built for you.

• Own and evolve end-to-end batch training pipelines for transaction-monitoring models.

• Build reliable software around the model lifecycle, including testing, CI/CD, versioning, deployment, monitoring, and rollback.

• Improve the maintainability, observability, and scalability of our model pipelines.

• Partner with platform and software engineers to make model delivery repeatable and safe.

• Build, maintain, and improve ML models for transaction monitoring, balancing detection quality, operational efficiency, explainability, and regulatory expectations.

• Engineer features that reflect AML and Fraud typologies and suspicious behaviours.

• Work with Risk investigators to translate domain knowledge into useful signals, alerting logic, and calibrated thresholds.

• Analyse the drivers of the AML Risk Score and recommend improvements to its features, logic, and thresholds.

• Define and track meaningful model and operational metrics, including detection performance, alert volumes, and investigator outcomes.

• Monitor drift and model health, run back-testing, and investigate changes in performance.

• Run sensitivity tests on synthetic datasets and assess how models behave across relevant scenarios and populations.

• Produce model cards, technical documentation, and other ML governance artefacts that support auditability and regulatory review.

• Contribute to system-design documentation and adapt solutions to regional compliance requirements.

• Partner with AML and Fraud Operations, Product, and Engineering to turn ambiguous problems into clear, scalable technical plans.

• Explain trade-offs clearly to both technical and non-technical stakeholders.

• Help the team improve its engineering practices, modelling approach, and understanding of financial-crime risk.

• Share what you learn and support a culture of thoughtful experimentation, constructive challenge, and continuous improvement.

• Strong production Python engineering experience. You write code that ships and are comfortable with automated testing, CI/CD, code review, versioning, observability, and operating services or pipelines in production.

• Experience deploying and operating ML models in…