Detect fraud in milliseconds, at the moment a
transaction happens, without blocking legitimate customers. Machine
Learning Engineer - Spain Madrid or Barcelona, Spain · Permanent ·
Hybrid What you'd actually work on 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 Where it gets
technically interesting 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 What we're looking for
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. The
company 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. Health insurance, adaptable
working, and an equity plan. Languages: Native or bilingual Spanish
and professional English. A search run by The French Sourcer,
recruitment built for technical teams.

📌 Machine Learning Engineer (Madrid)
🏢 The French Sourcer
📍 Madrid