Lead end-to-end ML lifecycle: data exploration, model training, evaluation, deployment, and monitoring. Design and scale batch and real-time feature pipelines, partner cross-functionally to productionize models, implement MLOps (CI/CD, retraining, drift detection, observability), and mentor engineers to maintain high code and system quality.

As a Data Scientist II, you will analyze fraud trends, optimize risk decision policies, and create dashboards to improve fraud model performance.