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Salary: USD 172,000 - 222,000 / annual
Virta Health is reversing metabolic disease through virtual care, personalized nutrition, and technology innovation. The company has raised over $350M and partners with major health plans, employers, and government organizations.
As Senior HEOR Scientist, you will lead real-world evidence (RWE) programs and health economics modeling to demonstrate Virta's clinical and economic value. You'll analyze rich clinical and claims datasets to generate publication-grade evidence that shapes how health plans, employers, and policymakers adopt virtual metabolic care.
Key responsibilities include:
- Develop and execute RWE programs and health economics models demonstrating clinical and economic value
- Translate real-world data from health plans and employers into actionable insights driving commercial growth and product innovation
- Publish findings in peer-reviewed journals and present at conferences and client meetings
- Partner cross-functionally with sales, marketing, and product teams to integrate evidence into commercial messaging and product development
- Serve as internal HEOR subject matter expert, advancing organizational research capabilities
In your first 90 days, you'll onboard with clinical and data science teams, take ownership of a claims dataset analysis project, align evidence needs with sales/marketing, draft an updated HEOR strategy roadmap, complete initial RWE analyses, and begin drafting peer-reviewed publications while establishing AI-enabled workflows.
REQUIREMENTS:
- Master's degree or PhD (preferred) in Health Economics, Public Health, Pharmacoeconomics, or related field
- 5+ years analyzing claims/clinical datasets, publishing in peer-reviewed journals, and presenting economic outcomes of clinical programs
- 3+ years business experience in startup or high-growth environment with proven ability to translate scientific evidence for commercial applications
- Proficiency in SQL, Python, R, or SAS for advanced claims analysis and data modeling
- Successfully designed and implemented repeatable AI-enabled workflows addressing team bottlenecks and improving efficiency
