Senior Data Scientist/ML Engineer - Financial Crime
On-siteBerlin, State of Berlin, Germany
Job Summary
Own and evolve end-to-end batch training pipelines for transaction-monitoring models, building reliable software around the model lifecycle including testing, CI/CD, versioning, deployment, and monitoring. Engineer features reflecting AML and Fraud typologies, partner with Risk investigators to translate domain knowledge into alerting logic, and define metrics to track detection performance and operational efficiency. Monitor drift and model health, run back-testing on synthetic datasets, and produce model cards and technical documentation to support auditability and regulatory review. Collaborate with AML, Fraud Operations, and Engineering teams to turn ambiguous problems into scalable technical plans while adapting solutions to regional compliance requirements.
Required Qualifications
- Strong production Python engineering experience
- Experience deploying and operating ML models in production
- Hands-on experience with end-to-end ML pipelines
- Solid data-engineering fundamentals
- A willingness to deepen your data-science expertise
- Clear, confident communication
Desired Qualifications
- Experience with PySpark or other distributed data-processing technologies
- Experience in AML, fraud detection, transaction monitoring, or another financial-crime domain
- Experience with unsupervised learning, such as anomaly detection or clustering
- Familiarity with feature stores, model registries, and alerting-threshold calibration
- Experience producing ML governance artefacts, such as model cards, validation reports, or audit documentation
- Experience with AI systems and tooling
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