Program Integrity Data Scientist III

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee the design, development, and implementation of sophisticated algorithms that identify claims for pre- and post-payment interventions related to potential Fraud, Waste, and Abuse.

• Evaluate and quantify issues concerning claim payments and suggest mitigations for risks to program integrity.

• Detect trends, risks, and patterns within healthcare datasets and propose strategic interventions.

• Guide analysts and data scientists through code evaluations, analytical assessments, and best-practice suggestions.

• Assess analytical, machine learning, and AI methodologies for fraud detection, anomaly recognition, and payment integrity.

• Perform outcome analyses for corporate programs and initiatives focused on payment integrity.

• Direct intricate data relationship analyses for payment integrity, anomaly detection, and FWA investigations.

• Track and investigate anomalies and emerging FWA trends throughout the organization.

• Partner with legal teams to produce data and analyses that support legal actions.

• Formulate hypothesis tests and extrapolations on statistically valid samples to identify outlier behavior and potential recoupment opportunities.

• Lead analytical strategies, project plans, methodologies, and investigative techniques.

• Create dashboards, visualizations, and reporting solutions that showcase model performance and program outcomes.

• Develop AI-powered and business intelligence dashboards that support investigator workflows, fraud detection, operational monitoring, and executive decision-making.

• Provide statistical validation and analysis of clinical program outcomes and interventions.

• Integrate analytical solutions with enterprise systems, platforms, and operational workflows.

• Present analytical insights and strategic recommendations to leadership and stakeholders.

• Spearhead the preparation and deployment of production-ready code, models, and automated decision-support solutions.

• Investigate policy, billing guidelines, and CMS directives to create innovative fraud, waste, and abuse concepts.

⛳️ Requirements

• A Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or a related discipline is required.

• Equivalent years of relevant work experience may be accepted in place of the required education.

• A minimum of five (5) years of experience in data analysis and/or analytic programming is necessary.

• Experience in healthcare is required.

• At least one (1) year of experience with cloud services such as Azure, AWS, or GCP and modern data stacks like Databricks or Snowflake is preferred.

• Experience in supporting payment integrity, fraud detection, audit recovery, SIU, or program integrity initiatives is preferred.

• Familiarity with developing and applying deep learning, neural networks, graph analytics, or graph neural network solutions is preferred.

• Advanced proficiency in SQL and at least one programming language such as Python or R is essential.

• Familiarity with SAS is preferred.

• Proficiency in designing and developing dashboards and reporting solutions using Power BI or similar platforms is required.

• Advanced skills in statistical analysis, including t-tests, ANOVAs, z-tests, statistical extrapolations, non-parametric significance testing, and sampling methodologies are necessary.

• In-depth knowledge of predictive modeling, machine learning, deep learning, neural networks, graph analytics, graph neural networks, clustering, dimensionality reduction, anomaly detection, and natural language processing is required.

• Proficiency in feature engineering and exploratory data analysis is essential.

• Knowledge of healthcare coding, billing processes, reimbursement methodologies, claims adjudication, and program integrity concepts is required.

• Proficiency with Microsoft Office applications, including Excel, PowerPoint, Word, and Access is necessary.

• Strong critical thinking, verbal communication, presentation, and written communication skills are essential.

• Ability to independently lead analytical initiatives and collaborate across cross-functional teams is required.

• Capability to mentor analysts and data scientists is necessary.

• No specific licensure or certification is required.

🏝️ Benefits

• Bonus opportunities tied to company and individual performance may be available.

• A comprehensive total rewards package is offered.

• Support for employee well-being is provided.

• An equal opportunity and belonging-focused workplace is emphasized.

• Up to 15% occasional travel for meetings, trainings, and conferences may be required.