CareSource is seeking to solve the problem of fraud, waste, and abuse (FWA) within its health plans by identifying and intervening on claims pre and post-pay, ensuring claim payment accuracy and mitigating FWA.
Requirements
- Strong expertise in statistical modeling, machine learning techniques, and predictive analytics tools such as Python, or R
- Proficiency with MS office (Excel, PowerPoint, Word, Access)
- Ability to perform advanced statistical analyses and techniques including t-tests, ANOVAs, z-tests, statistical extrapolations, non-parametric significance testing, and sampling methodologies
- Expertise in legal, auditing, and investigative services, as well as proficiency in statistical modeling and anomaly detection
- Extensive knowledge of predictive modeling, machine learning, and artificial intelligence
- Familiarity with healthcare data sets, including claims data (Professional, facility, pharmacy), electronic health records (EHR), and population health data
- Proficient in feature engineering techniques and exploratory data analysis
Responsibilities
- Oversee and manage an ever-changing portfolio of claim-centric algorithms that identify claims pre and post-pay that can be moved into various workflows for intervention – including a request for medical record and audit (correct coding and medical necessity), a downcode to revised reimbursement, etc.
- Hands-on management of data science function, including technical development and / or direct oversight of technical work product developed by the team.
- Assist in the deployment of advanced analytic solutions into the various functions within Program Integrity (Investigations, Regulatory, Audit, Prepay, etc.).
- Conduct outcome analyses to determine impact and effectiveness of corporate and Special Investigations Unit (SIU) initiatives.
- Develop hypothesis tests and extrapolations on statistically valid samples to establish outlier behavior patterns and potential recoupment.
- Use descriptive statistical techniques to measure impact of various actions/studies, internal and external, develop sampling and hypothesis testing to help the organization determine outcomes.
- Develop and implement predictive models, algorithms, and statistical techniques to extract insights from large and complex healthcare datasets.
Other
- Bachelor's degree in Data Science, Mathematics, Statistics, Criminal Justice, Medical/Health Care Field, or another related field required
- Master's degree in Data Science, Mathematics, Statistics, Criminal Justice, Medical/Health Care Field, or another related field preferred
- Six (6) years Data Science, Health Care, Legal, Auditing, Claims and/or Investigative Services required
- One (1) year Cloud Services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflakes) required
- Two (2) years of leadership/supervisory experience required
