Principal Data Scientist, Fraud Modelling

Hybrid

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Responsibilities

Conduct R&D on fraud detection and risk monitoring across the digital advertising ecosystem, including attribution fraud, lead fraud, click injection, browser extension abuse (e.g., Honey-style coupon hijacking), brand safety violations, and creator authenticity verification.
Design, prototype, and validate ML models and rule-based systems for fraud detection, partner risk scoring, compliance monitoring, and trust & safety workflows.
Research and apply graph-based fraud detection techniques (community detection, link analysis, behavioral clustering) and explore graph database applications for modeling relationships between users, devices, transactions, and partners to uncover coordinated fraud rings and suspicious network patterns.
Deploy Fraud and Risk ML models to production; own the end-to-end delivery from ETL, feature engineering, model training, deployment, to monitoring.
Perform deep-dive analyses on fraud trends, partner behavior, and risk patterns to inform model strategy and business decisions.
Build dashboards and reports to communicate model performance, fraud impact, and risk metrics to leadership. Cross-functional collaboration

In this role, you'll be at the forefront of protecting our affiliate marketing ecosystem by researching, developing, and deploying ML models that detect and prevent fraud across attribution, lead quality, and partner compliance.
Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, or similar). •
Experience with feature engineering, model evaluation (ROC/AUC, precision-recall, cost-sensitive learning), and handling imbalanced datasets.
Familiarity with production ML workflows (versioning, monitoring, A/B testing, model retraining).
Analytical rigor : Strong foundation in statistics and ML; ability to design experiments, validate models, and interpret results with business context.
experience presenting to cross-functional teams.
Education : Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred. Preferred / Nice to have •
Experience in affiliate marketing, ad tech, or e-commerce fraud (attribution fraud, click fraud, lead validation, coupon abuse).