Staff Data Scientist, Risk and Fraud

Staff Data Scientist, Risk and Fraud leads fraud modeling and operations for banking/risk domains, owning the fraud/risk modeling lifecycle with independence to resolve complex issues and communicate with cross-functional partners. Establish and enforce robust testing, model governance, and data science best practices to ensure high-quality, compliant, and scalable outputs. Design and maintain advanced fraud models for banking/subprime lending using predictive modeling, statistical analysis, and machine learning (e.g., XGBoost, Deep Learning) to detect fraud/risk. Serve as the primary technical partner for Fraud/Risk Strategy, Operations, and Engineering teams, ensuring model outputs are accurate and producible in production. Provide mentorship and foster a culture of technical excellence while implementing monitoring frameworks to track model performance and data integrity in real-time, aligning with Model Risk Management standards.