Machine Learning Engineer

• Building and maintaining fraud detection and risk-scoring models used on live transactions
• Developing features from transactional, behavioural, account, and device data
• Training and evaluating models against new and evolving fraud patterns
• Deploying models into low-latency production systems
• Reducing false positives while maintaining effective fraud detection rates
• Working with risk specialists to translate fraud scenarios and business rules into model features
• Designing feedback loops using confirmed fraud cases, manual reviews, and transaction outcomes
• Monitoring model performance, feature quality, drift, latency, and prediction distributions
• Investigating model degradation and changes in customer or fraud behaviour
• Improving model deployment, versioning, retraining, and rollback processes
• Documenting model behaviour and decisions for engineers, risk teams, and auditors
• Contributing to code reviews, automated testing, CI/CD, and ML engineering standards, * Real-time inference under strict latency constraints, with decisions required before transactions are completed
• Highly imbalanced datasets where confirmed fraud represents only a small proportion of all transactions
• Fraud patterns that change deliberately in response to existing detection methods
• Managing delayed or incomplete labels when transaction outcomes are not immediately known
• Balancing fraud detection rates against the commercial and customer impact of false positives
• Identifying drift in models and features before it results in significant financial losses
• Combining machine learning outputs with business rules and manual risk controls
• Meeting explainability and traceability requirements for decisions that may need to be reviewed later
• Rolling out new models safely through controlled testing, monitoring, and rollback mechanisms

• 3+ years of experience developing applied machine learning models
• Strong Python skills and good software engineering practices
• Experience deploying and operating models in production
• Knowledge of classification, anomaly detection, or risk-scoring methods
• Experience working with imbalanced datasets and appropriate evaluation metrics
• Understanding of precision, recall, false-positive rates, and the business trade-offs between them
• Experience with model monitoring, drift detection, versioning, and retraining
• Ability to work with large transactional or behavioural datasets
• Experience with low-latency inference systems
• Confidence working with risk, data engineering, platform, and product teams
• Experience using Git, code reviews, automated testing, and CI/CD

Previous experience in fraud, payments, insurance, credit risk, or anomaly detection would be valuable, but it is not required if you have worked on comparable production machine learning problems.

A fintech or insurtech scale-up operating across Southern Europe, with several hundred employees and high daily transaction volumes.

The machine learning team works closely with risk and engineering to improve fraud detection while limiting unnecessary friction for legitimate customers.