Senior Data Scientist - Fraud Model Validation (Fintech)

Location: Milan, Italy; hybrid work with most teams meeting in the office 2–3 days per week

Salary: €72,950–€93,142 per year

Company

A fintech platform building an everyday finance network for more than 120 million consumers across 26 countries, using AI to improve financial services.

What you will do

• Independently validate fraud models for payments, logins, identity, and account takeover scenarios.
• Reproduce model results, build challenger models, and assess precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, and fraud capture rate.
• Review transaction datasets exceeding 100 million records and feature pipelines with hundreds of features for representativeness, leakage, and bias.
• Evaluate drift detection, retraining strategies, production monitoring, CI/CD, and deployment controls across Docker, Jenkins, and AWS.
• Validate graph models, behavioral biometrics, anomaly detection, and GenAI-based systems.
• Document validation outcomes and communicate model risks to data scientists, ML engineers, and business stakeholders.

Requirements

• 3+ years of hands-on experience with fraud-related modeling, including transaction, account takeover, identity, or payments fraud.
• Strong knowledge of LightGBM, anomaly detection, and graph or network models.
• Experience across the ML lifecycle, including feature engineering, deployment, monitoring, and drift detection.
• Fluency in Python and SQL, with experience using PySpark or Spark at scale.
• Experience designing agentic AI workflows and understanding model validation, risk governance, bias, fairness, explainability, and privacy.
• Ability to work in a hybrid setup in Milan and attend the office 2–3 days per week, depending on the team.

Nice to have

• Master’s or PhD in data science, statistics, mathematics, computer science, physics, engineering, or another quantitative field.
• Experience with BNPL, credit cards, or other transaction-heavy payment products.
• Experience mentoring junior validators or leading validation reviews.
• Knowledge of rejected-transaction inference, AI governance frameworks, and emerging AI regulatory requirements.

Culture & Benefits

• Independent second-line validation role working closely with first-line fraud data science and ML engineering teams.
• Co-located collaboration with office attendance varying by team and business needs.
• Inclusive environment welcoming diverse skills, perspectives, and backgrounds.
• Final compensation depends on qualifications, skills, and experience.

Hiring process

• Submit a CV in English focused on concrete results, deliverables, and business impact.