The Mission

Financial crime is constantly changing — new patterns and behaviours emerge all the time, and the data SumUp works with is complex. As a Senior Data Science/ML Engineer in the Risk AI Engineering Squad, you'll build the production systems that turn machine learning into reliable, explainable transaction-monitoring capabilities — making financial activity safer and more trustworthy for merchants, customers and SumUp.

What You'll Do

Build ML systems that work in production: own and evolve end-to-end batch training pipelines for transaction-monitoring models; build reliable software around the model lifecycle — testing, CI/CD, versioning, deployment, monitoring, rollback
Turn data and domain knowledge into better detection: build and maintain ML models for transaction monitoring, balancing detection quality, operational efficiency, explainability and regulatory expectations; engineer features that reflect AML and fraud typologies
Keep models trustworthy over time: track detection performance, alert volumes and investigator outcomes; monitor drift and model health, run back-testing, and investigate performance changes; run sensitivity tests on synthetic datasets
Own the governance: produce model cards, technical documentation and ML governance artefacts that support auditability and regulatory review

Who You Are

Strongest on the engineering side of machine learning, with growing data-science depth — or the reverse; you enjoy working across both disciplines
Comfortable building production-ready software while staying close to the data, models and decisions those systems support
You'll join a cross-functional team within the Risk & Compliance tribe, working with AML and Fraud Operations, investigators, Product and Engineering

Why Join

This is a high-stakes, high-impact role: making ML work reliably where it matters most — keeping financial crime harder to hide — across SumUp's 38 markets.

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The Mission

What You'll Do

Build ML systems that work in production: own and evolve end-to-end batch training pipelines for transaction-monitoring models; build reliable software around the model lifecycle — testing, CI/CD, versioning, deployment, monitoring, rollback
Turn data and domain knowledge into better detection: build and maintain ML models for transaction monitoring, balancing detection quality, operational efficiency, explainability and regulatory expectations; engineer features that reflect AML and fraud typologies
Keep models trustworthy over time: track detection performance, alert volumes and investigator outcomes; monitor drift and model health, run back-testing, and investigate performance changes; run sensitivity tests on synthetic datasets
Own the governance: produce model cards, technical documentation and ML governance artefacts that support auditability and regulatory review

Who You Are

Strongest on the engineering side of machine learning, with growing data-science depth — or the reverse; you enjoy working across both disciplines
Comfortable building production-ready software while staying close to the data, models and decisions those systems support
You'll join a cross-functional team within the Risk & Compliance tribe, working with AML and Fraud Operations, investigators, Product and Engineering

Why Join

This is a high-stakes, high-impact role: making ML work reliably where it matters most — keeping financial crime harder to hide — across SumUp's 38 markets.