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Job DescriptionOur work depends on a Data Scientist Manager joining our team to support Centers for Medicare and Medicaid Services anti-fraud activities. As a Data Scientist Manager supporting the Healthcare Fraud Prevention Partnership (HFPP), you will manage a team of Data Scientists engaged in Fraud, Waste and Abuse (FWA) analytics, collaborating with FWA Subject Matter Experts and Business Intelligence Developers to develop to develop FWA models and algorithms that detect and describe actionable FWA leads for the HFPP.
The Data Scientist Manager directs the Data Science team in using tools such as SAS and Python to analyze healthcare claims across Medicare, Medicaid and private payers within a multi-billion record database made up of a large partnership of public and private healthcare payers. Your Data Science team will be a part of a 5-person team supporting the Trusted Third Party fraud, waste and abuse data analytics.
HOW A DATA SCIENTIST MANAGER WILL MAKE AN IMPACT:
This position manages a team of Data Scientists, embedded within a multi-disciplinary organization of Data Scientists, Business Intelligence Analysts and FWA Subject Matter Experts. Our team is 100% remote and distributed throughout the country.
This position will be responsible for planning, directing and overseeing, development and execution of FWA models and algorithms in multi-payer environments by creating, reviewing, and maintaining statistical analysis plans, providing data interpretation and generating reports in support of proposed research questions.
The Data Scientist Manager will facilitate the Data Science team’s collaboration with FWA Subject Matter Experts, Business Intelligence Developers, and clients, to develop meaningful analyses for FWA modeling results and provide predictive analytic statistical support.
The Data Scientist Manager selected for this position will direct the design and maintenance of automated data pipelines, in collaboration with data engineers and Business Intelligence Developers, feeding analytic outputs to a sever environment where HFPP Partners will interact with their analytic outputs.
The Data Scientist Manager must have a proven track record delivering outstanding presentations to clients, stakeholders, and effectively communicate the key insights and value propositions of the TTP analytic outputs.
The Data Scientist Manager will not only be a manager (40%) and developer/coder (60%), they will also oversees quality review on analytic output to ensure consistency with protocol and adequacy to meet methodological objectives, and to ensure compliance with applicable access standards.
The Data Scientist Manager will document processes, best practices, and strategies for existing and future FWA models and algorithms on behalf of the Data Science team, optimizing business efficiencies as it relates to process improvement.
This team member will manage a team of 5-8 Data Scientists.
WHAT YOU'LL NEED TO SUCCEED:
Master’s Degree in Statistics or related field or an equivalent combination of education and experience.
7 years experience in statistical and computer-science related field(s).
Experience using SQL and Python software for healthcare Fraud, Waste and Abuse datamining and statistical modeling.
Experience with client relation activities, ingesting and synthesizing customer requirements, and effectively communicating complex healthcare analytic outcomes to client satisfaction.
Experience successfully presenting FWA analytic outcomes to clients, stakeholders, and conferences.
Expertise in medical terminology and all healthcare coding systems (e.g., ICD-10, CPT, HCPCS, DRG and the like).
Experience datamining for healthcare fraud, waste and abuse in large, multi-payer databases with diverse health care benefit structures and claim processing systems, including private plans (commercial insurance) as well as Federal and State public plans (Medicare and Medicaid).
Experience researching, interpreting and applying payer medical (coverage) policies (e.g., LCDs, NCDs, private carrier medical policies) and common industry claim edits (e.g., NCCI).
Prior experience managing multi-person data analytics teams with a focus on healthcare data; strong supervisory experience leading project teams and/or mentorship ability guiding new analysts.
Demonstrated expertise in the complete Microsoft Office Suite including: Word, Excel, and PowerPoint (candidate may be called upon to show examples from portfolio or submit practicum assignments).
DESIRED QUALIFICATIONS AND EXPERIENCE:
Experience in Amazon Web Services (AWS) cloud and/or Snowflake data warehouse environments.
Ability to communicate technical outcomes with a high degree of detail and precision to technical audiences, while at the same time being able to communicate those outcomes to non-technical audiences in an approachable and understandable manner.
Exceptional problem-solving abilities, accuracy with work, strong organizational skills, attention to detail and the ability to multi-task while meeting deadlines.
Excellent communication skills, both written and verbal, and especially the ability to communicate data clearly to clients.
Ability to work in a fast-paced, team-oriented environment with minimal supervision.
SECURITY CLEARANCE LEVEL:
The candidate selected for this role must be eligible to obtain a public trust clearance. This requires residency in the U.S. for 3 of the last 5 years.
WHAT GDIT CAN OFFER YOU:
The GDIT HFPP Trusted Third Party is the only data warehouse of its type anywhere, bringing in many billions of healthcare claims from dozens of healthcare payers, including federal, state and local plans both public and private. We use this data warehouse solely for fraud, waste and abuse analytics.
The TTP is responsible for identifying overpayments, fraud leads, and meaningful insights about abusive billing behaviors through a cross-payer lens. Government and private healthcare. organizations collaborated to form the HFPP as a public-private partnership to combat fraud, waste, and abuse.
Competitive pay and benefits, flexible work schedule, collaborative inter-disciplinary environment where the best idea wins.
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