Machine Learning Engineer - Fraud Risk

Key Responsibilities Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams Requirements 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)