Lead Data Scientist, Fraud Modelling
Core
Lead Data Scientist specializing in Fraud and Risk to protect the affiliate marketing ecosystem by researching, developing, and deploying ML models that detect and prevent fraud across attribution, lead quality, and partner compliance.
Role type
Lead Data Scientist (Fraud & Risk)
Builds
Production ML models and rule-based systems for fraud detection, partner risk scoring, and compliance monitoring.
Domain
Digital advertising, affiliate marketing, fraud detection
Deliverable
production ML models
Required skills
Python, SQL, scikit-learn, XGBoost, LightGBM, feature engineering, model evaluation (ROC/AUC, precision-recall), handling imbalanced datasets, production ML workflows, statistics, graph-based fraud detection, community detection, link analysis, behavioral clustering
Preferred skills
affiliate marketing fraud experience, browser extension detection, fingerprinting, device/user identity resolution, privacy-preserving ML, real-time scoring, GCP tools (BigQuery, Vertex AI), Databricks/Spark, rule engines
Technologies
Python, SQL, scikit-learn, XGBoost, LightGBM, GCP (BigQuery, Vertex AI, Cloud Run), Databricks, Spark
Responsibilities
Conduct R&D on fraud detection and risk monitoring across the digital advertising ecosystem; Design, prototype, and validate ML models and rule-based systems for fraud detection; Deploy Fraud and Risk ML models to production; Perform deep-dive analyses on fraud trends and risk patterns; Build dashboards and reports to communicate model performance and risk metrics to leadership.
Seniority
Lead, hands-on IC with mentorship responsibilities