Design and improve fraud detection systems that prioritize high-risk claims, optimizing the trade-off between detection accuracy and operational efficiency
Collaborate across teams and functions, working closely with fraud analysts, engineers, and product managers
Develop deep expertise in fraud dynamics, identifying emerging fraud patterns, understanding how they evolve across the insurance lifecycle, and translating these insights into effective detection and prevention strategies, for example identifying suspicious networks of related claims, synthetic entities, or anomalous behavioral patterns

At least 3 years of experience in analytical fraud detection in the Insurance, Finance or Law Enforcement domain
Applied knowledge of network analysis, anomaly detection or risk scoring and segmentation to real fraud problems
Strong Python programming skills, especially in the data domain, and fluent use of Git
Knowledge of Spark, SQL or any other data-oriented programming language and proficiency with standard data wrangling and modeling libraries (Pandas, Sklearn, Keras, XGBoost, LightGBM, etc.)
Nice-to-Have: Experience in motor insurance
In-depth knowledge of graph databases (Neo4j, AWS Neptune) and analytics (NetworkX, GNN) or explainability applied to anomaly detection
Knowledge of BI tools (Tableau or similar)
Proficiency in Italian and/or Spanish

Senior Data Scientist

Key skills