Senior Data Scientist/ML Engineer - Financial Crime

As a Senior Data Scientist/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.

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

What you'll need

• Strong production Python engineering experience and comfortable with automated testing, CI/CD, code review, versioning, observability, and operating services or pipelines in production.
• Experience deploying and operating ML models in production, including reproducible training, model versioning, deployment, monitoring, incident response, and rollback.
• Hands-on experience with end-to-end ML pipelines, from data preparation and training through validation and production use.
• Solid data-engineering fundamentals and experience with complex, multi-source data ecosystems.
• A willingness to deepen your data-science expertise in modelling, feature engineering, evaluation, and experimentation.
• Clear, confident communication to align stakeholders, set expectations, and surface risks.

Nice to have

• 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.
• Experience producing ML governance artefacts, such as model cards, validation reports, or audit documentation.

What they offer

• Work with SumUp globally on large-scale fintech products from the Berlin office.
• Stock option programme to own a stake in SumUp's future success.
• Annual L&D budget of €2,000 for conferences and further education.
• Corporate pension scheme with up to 20% matching of contributions.
• 28 days of paid leave.