The opportunity
Financial crime is constantly changing. New patterns and behaviours emerge all the time, and the data we work with is complex.
What you'll do
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.
What they're looking for
- 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.
