About the company
At SumUp, we're on a mission to empower small businesses across the globe by providing simple and affordable tools that allow them to thrive. Today, over 4 million businesses in 37 markets rely on SumUp as their financial partner to manage payments, finance and customer relationships.
Responsibilities
- Build and ship production ML systems end to end: own and evolve end-to-end batch training pipelines, model versioning, monitoring, model deployment, and rollback for transaction monitoring models.
- Build, maintain, and improve ML models for transaction monitoring, focusing on detection quality, operational efficiency, and regulatory compliance
- Engineer features mapped to AML and Fraud typologies and suspicious behaviours, working closely with Risk investigators to translate domain knowledge into alerting logic and threshold calibration
- Run sensitivity tests on synthetic datasets, produce ML governance artefacts such as model cards, and deliver audit-ready documentation to meet regulatory expectations
- Own and evolve the AML Risk Score by analysing driver contributions, monitoring drift, running back-testing, and recommending improvements to features, logic, and thresholds
- Partner with AML and Fraud Operations, Product, and Engineering to translate stakeholder needs into actionable, scalable data science solutions
- Track and improve detection performance metrics, adapt solutions to regional compliance requirements, and contribute to system design documentation
Requirements
- Production Python engineering: you write code that ships — you're comfortable with CI/CD, automated testing, versioning, and monitoring in a real production environment
- End-to-end ML pipeline experience: demonstrated experience deploying and operating ML models in production, including drift monitoring and rollback
- Modelling and productionalizing models: demonstrated experience training ML models, choosing appropriate KPIs and metrics for evaluation.
- Data engineering fundamentals: hands-on experience with complex, multi-source data ecosystems, data quality, and lineage
- Clear, confident communication: ability to align cross-functional stakeholders, set expectations, and turn ambiguous compliance requirements into a concrete technical plan
Nice to have:
- Experience with Pyspark
- Experience in AML, fraud detection, or financial crime domains
- Unsupervised machine learning (e.g. anomaly detection, clustering)
- Familiarity with Feature Stores and alerting threshold calibration
- Experience producing regulatory ML governance artefacts (model cards, audit documentation)
- Experience with AI systems and tooling
Conditions
- 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