Program Integrity Data Scientist III

Job Summary:

The Program Integrity Data Scientist III is responsible for leading the design, development, and deployment of advanced analytical solutions that support payment accuracy, anomaly detection, and Fraud, Waste, and Abuse (FWA) prevention and recovery efforts. This role serves as a senior analytical resource, providing technical leadership across complex investigations, predictive modeling initiatives, and enterprise program integrity strategies while mentoring junior analysts and data scientists.

Essential Functions:

• Lead the design, development, and implementation of advanced algorithms that identify claims for pre- and post-payment intervention related to potential Fraud, Waste, and Abuse.
• Analyze and quantify claim payment issues and provide recommendations to mitigate identified program integrity risks.
• Identify emerging trends, risks, and patterns across multiple healthcare datasets and recommend strategic interventions to improve program integrity outcomes.
• Mentor and provide technical guidance to analysts and data scientists through code reviews, analytical reviews, and best practice recommendations.
• Evaluate emerging analytical, machine learning, and AI techniques to improve fraud detection, anomaly identification, and payment integrity efforts.
• Conduct outcome analyses to determine impact and effectiveness of corporate program and payment integrity initiatives.
• Lead the examination and interpretation of complex data relationships to support payment integrity, anomaly detection, and FWA investigations.
• Lead monitoring and investigation of anomalies and emerging trends associated with potential Fraud, Waste, and Abuse activities across the enterprise.
• Collaborate with the legal department, generating data and analyses to support legal proceedings.
• Develop hypothesis tests and extrapolations on statistically valid samples to establish outlier behavior patterns and potential recoupment.
• Lead development and communication of analytical strategies, project plans, methodologies, and investigative approaches for complex initiatives.
• Design and develop advanced dashboards, visualizations, and reporting solutions that demonstrate model performance, program effectiveness, and program integrity outcomes.
• Lead development of AI-enabled and business intelligence dashboards that support investigator workflows, fraud detection activities, operational monitoring, and executive decision-making.
• Provide statistical validation and analysis of outcomes associated with clinical programs and interventions.
• Provide technical leadership in integrating analytical solutions with enterprise systems, platforms, and operational workflows.
• Present analytical findings, strategic recommendations, and program integrity insights to leadership and key stakeholders.
• Lead preparation and deployment of production-ready analytical code, models, and automated decision-support solutions.
• Research policy, billing guidelines, CMS direction and others to ideate new fraud, waste and abuse concepts to implement.
• Perform any other job related duties as requested.

Education and Experience:

• Bachelor’s degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or a related field required
• Equivalent years of relevant work experience may be accepted in lieu of required education
• Five (5) years data analysis and/or analytic programming required
• One (1) year Experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflake) preferred
• Healthcare experience required
• Experience supporting payment integrity, fraud detection, audit recovery, SIU, or program integrity initiatives preferred
• Experience developing and applying deep learning, neural network, graph analytics, or graph neural network (GNN) solutions for fraud detection, anomaly identification, payment integrity, or healthcare analytics initiatives preferred
  Competencies, Knowledge and
  
  Skills:
  
  
• Advanced proficiency in SQL and at least one programming language such as Python or R
• Familiarity with SAS is preferred
• Proficient in designing and developing dashboards and reporting solutions using Power BI or similar business intelligence platforms
• Ability to perform advanced statistical analyses and techniques including t-tests, ANOVAs, z-tests, statistical extrapolations, non‑parametric significance testing, and sampling methodologies
• Advanced knowledge of predictive modeling, machine learning, deep learning, neural networks, graph analytics, graph neural networks…