Data scientist in financial crime

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 SumUp. 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.

• 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 production. You understand the practical realities beyond experimentation: reproducible training, model versioning, deployment, monitoring, incident response, and rollback.
• Hands-on experience with end-to-end ML pipelines. You have taken models from data preparation and training through validation and production use, and you understand how to choose appropriate KPIs and evaluation metrics.
• Solid data-engineering fundamentals. You have worked with complex, multi-source data ecosystems and care about data quality, lineage, reproducibility, and failure modes.
• A willingness to deepen your data-science expertise. You are interested in modelling, feature engineering, evaluation, and experimentation — even if your background is primarily in ML engineering or software engineering.
• Clear, confident communication. You can align stakeholders, set expectations, surface risks, and turn ambiguous compliance or operational requirements into a concrete technical plan.
• Будет плюсом: 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

• Opportunity to work with SumUppers globally on large-scale fintech products used by millions of businesses worldwide, from our Berlin office. This involves an office-first setup
• Commitment to Diversity and Inclusion: be part of a workplace that values and promotes diversity, fostering an inclusive environment where everyone's perspectives are respected and embraced
• Enrolment onto our Virtual Stock Option programme: you will own a stake in SumUp's future success
• A dedicated annual L&D budget of €2,000 for your individual development, which can be used to attend conferences and/or advance your career through further education
• A corporate pension scheme where we match up to 20% of your contributions
• Generous time off: enjoy 28 days of paid leave plus public holidays and special leave days
• Numerous other benefits such as Urban Sports Club subsidy, Kita placement assistance, and subsidised office lunches
• Break4me: 1-month sabbatical after 3 years of service
• Referral Bonus: earn additional rewards by referring talented individuals to join the SumUp team