On the fraud side, you will combine real-time ML, graph analytics, and explainable AI to detect emerging fraud, waste, and abuse across claims, enrollment, provider, and encounter data; uncover collusive networks and billing anomalies; and translate those signals into defensible investigative leads, preventive controls, and KPI visibility leaders can use immediately. Fraud, Waste & Abuse Analytics & ML Ops: Design, build, and operationalize supervised and unsupervised models, including classification, clustering, anomaly detection, risk scoring, and graph/network analysis, to detect known and emerging FWA patterns across claims, enrollment, provider, and encounter data.