Inovalon was founded in 1998 on the belief that technology, and data specifically, would empower the transformation of the entire healthcare ecosystem for the better, improving both outcomes and economics. At Inovalon, we believe that when our customers are successful in their missions, healthcare improves. Therefore, we focus on empowering them with data-driven solutions. And the momentum is building.
Together, as ONE Inovalon, we are a united force delivering solutions that address healthcare’s greatest needs. Through our mission-based culture of inclusion and innovation, our organization brings value not just to our customers, but to the millions of patients and members they serve.
This position is remote. Employees residing within a 40-mile radius of our offices in Canonsburg, PA; Tampa, FL; or Bowie, MD are required to follow a hybrid schedule, working onsite Tuesdays and Wednesdays.
Overview: The Senior Health Data Scientist will lead and serve as subject matter expert to highly complex data extracts, programming, and analytical modeling capabilities in support of the team’s services to internal and external clients. The Senior position provides guidance and support as needed to junior staff. The team consists of staff with specialized experience, such as statisticians, researchers, health economists, clinicians, and project managers, who conceptualize, execute, and deliver qualitative, analytical, and strategy-based work products for clients. We also work closely with other product teams and business units, applying the full scope of the company’s knowledge base to the development of multi-disciplinary, creative solutions on behalf of our clients. Business Unit : Insights - Health Data Science Team
This team is the data subject matter expert within the Insights business unit. Their role is to support teams across the organization to bridge the gap between product and engineering. Assist with translating the high-level product vision from the product lead by:
1) Writing detailed logic/algorithm requirements
2) Building prototypes
3) Best practices consulting, and
4) User acceptance testing.
This also involves data profiling, data investigation, root-cause analyses, etc.
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