Fraud-Detection Data Scientist (Production ML)

Moniepoint Group; Barcelona; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Moniepoint Group is seeking a Data Scientist to join our fraud analytics team in Barcelona. You will prototype, evaluate, and deploy ML models to detect fraud, and you will design experiments to balance customer experience with loss reduction.

You will quantify fraud typologies and build anomaly detection systems, working closely with engineers, product managers, and analysts to turn insights into production-ready mitigations.

Formación

• 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.
• Hands-on experience building and deploying machine learning models in production.
• Solid grounding in statistics, experimentation, model evaluation, and feature engineering.

Responsabilidades

• Prototype, evaluate, and help produce ML models for fraud detection; own monitoring and retraining cycles.
• Design and run experiments to measure impact of fraud interventions, balancing customer experience against loss reduction.
• Size fraud typologies across product lines to inform prioritization and investment decisions.
• Build and maintain anomaly detection systems to surface novel fraud vectors before they scale.
• Collaborate with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations.

Conocimientos

Python

SQL

Experiment design

Fraud analytics

Educación

Quantitative degree (Statistics / Mathematics / Engineering / CS)

Descripción del empleo

Moniepoint Group is seeking a Data Scientist to join our fraud analytics team in Barcelona. You will prototype, evaluate, and deploy ML models to detect fraud, and you will design experiments to balance customer experience with loss reduction.

You will quantify fraud typologies and build anomaly detection systems, working closely with engineers, product managers, and analysts to turn insights into production-ready mitigations.