Real-Time Fraud ML Engineer | Low-Latency Risk Scoring

The French Sourcer; Madrid; Híbrido; EUR 55.000 - 75.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Descripción de la vacante

The French Sourcer in Madrid/Barcelona is seeking a Machine Learning Engineer to design and deploy fraud-detection models for real-time decisions. You will build features from transactional data, monitor drifting models and latency, and collaborate with risk, data engineering, platform, and product teams to balance detection rates with customer experience.

Ideal candidates have 3+ years in applied ML, strong Python skills, and experience deploying models in production using CI/CD pipelines.

Formación

• 3+ years of experience building applied ML models.
• Strong Python skills and solid software engineering practices.
• Experience deploying and operating models in production.
• Experience with classification, anomaly detection, or risk-scoring methods.
• Experience with imbalanced datasets and evaluation metrics.
• Understanding precision/recall and business trade-offs.

Responsabilidades

• Build and maintain fraud detection and risk-scoring models for live transactions.
• Develop features from transactional, behavioural, account, and device data.
• Train and evaluate models against evolving fraud patterns.
• Deploy models into low-latency production systems.
• Reduce false positives while maintaining detection rates.
• Collaborate with risk and engineering to translate fraud scenarios into features.
• Design feedback loops using fraud cases and outcomes.
• Monitor model performance, drift, latency, and distribution.

Conocimientos

Python

Production deployment

CI/CD

Model monitoring

Low-latency inference

Imbalanced data handling

Git and code reviews

Experiment tracking

Herramientas

Git

Descripción del empleo

The French Sourcer in Madrid/Barcelona is seeking a Machine Learning Engineer to design and deploy fraud-detection models for real-time decisions. You will build features from transactional data, monitor drifting models and latency, and collaborate with risk, data engineering, platform, and product teams to balance detection rates with customer experience.

Ideal candidates have 3+ years in applied ML, strong Python skills, and experience deploying models in production using CI/CD pipelines.