Sr. Fraud, Waste, and Abuse Data Analyst

Data Analysis & Fraud Detection

• Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools
• Identify patterns and anomalies that may indicate fraud, waste, or abuse, including visit overlaps, impossible or implausible service combinations, inflated or duplicate billing, provider billing spikes, and outlier utilization patterns
• Identify inconsistencies in electronic visit verification (EVV) data and suspicious provider enrollment or credentialing indicators
• Detect patterns indicative of upcoding, place-of-service manipulation, or beneficiary identity issues
• Develop and refine detection queries and analytical logic that can be applied across datasets at scale
• Conduct proactive data analysis to identify emerging fraud patterns and program integrity risks
• Apply knowledge of end-to-end revenue cycle including claims submission, adjudication, remittance, and denial/appeal workflows

AI & Advanced Analytics

• Apply machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering of suspicious billing behavior
• Collaborate with data science teams on feature engineering, model validation, and operationalization of AI-driven detection logic
• Leverage generative AI and LLM-based tools to support investigation summarization and pattern analysis