The business problem CareSource is looking to solve is to develop, implement, manage, and deploy in-depth analyses that meet the information needs associated with payment accuracy, anomaly detection, and Fraud, Waste, and Abuse (FWA) in the healthcare industry.
Requirements
- Proficient in SQL and at least one of the following programming languages: Python / R / RAT STAT
- Familiarity with SAS is preferred
- Preferred beginner level of knowledge of developing reports or dashboards in Power BI or other business intelligence applications
- Ability to perform advanced statistical analyses and techniques including t-tests, ANOVAs, z-tests, statistical extrapolations, non-parametric significance testing, and sampling methodologies
- Working knowledge of predictive modeling and machine learning algorithms such as generalized linear models, non-linear supervised learning models, clustering, decision trees, dimensionality reduction and natural language processing
- Proficient in feature engineering techniques and exploratory data analysis
- Familiarity with optimization techniques and artificial intelligence methods
Responsibilities
- Build concepts as algorithms that identify claims for pre- or post-pay intervention based on probability of fraud, waste, and abuse.
- Analyze and quantify claim payment issues and provide recommendations to mitigate identified program integrity risks.
- Identify trends and patterns using standard corporate, processes, tools, reports and databases as well as leveraging other processes and data sources.
- Conduct outcome analyses to determine impact and effectiveness of corporate program and payment integrity initiatives.
- Collaborate on the examination and explanation of complex data relationships to answer questions identified either within the department or by other departments as it relates to payment accuracy, anomaly detection, and FWA.
- Monitoring of and providing explanation of anomalies related to trends associated with the potential for Fraud Waste and Abuse across the corporate enterprise.
- Develop hypothesis tests and extrapolations on statistically valid samples to establish outlier behavior patterns and potential recoupment.
Other
- Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or a related field required
- Three (3) years data analysis and/or analytic programming required
- Healthcare experience required
- Up to 15% (occasional) travel to attend meetings, trainings, and conferences may be required
- Demonstrated critical thinking, verbal communication, presentation and written communication skills
