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Director, Applied Science

Garner Health
CompanyGarner Health
CategoryScience & Research
LocationNew York City
RemoteOn-site (inferred)
EmploymentNot stated
LevelDirector
SalaryNot stated by the employer
Posted27 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
Garner’s mission is to transform the healthcare economy, delivering high-quality and affordable care for all.  We are fundamentally reimagining how healthcare works in the U.S. by partnering with employers to redesign healthcare benefits using clear incentives and powerful, data-driven insights. Our approach guides employees to higher-quality, lower-cost care, creating a system that works better for everyone. Patients achieve better health outcomes, employers spend healthcare dollars more effectively, and physicians are rewarded for delivering exceptional care rather than performing more procedures.  Garner is one of the fastest-growing healthcare technology companies in the country. Our products are trusted by the most sophisticated employers and providers in the industry, and we are building a team of talented, mission-driven individuals who are motivated to make a meaningful impact on healthcare at scale. About the role: We are seeking an exceptional Director of Applied Science to lead Garner Health Intelligence (GHI). The backbone of all of Garner’s products, GHI is responsible for Garner’s best-in-class doctor rankings across every medical subspecialty, as well as other foundational data assets. GHI is also the product owner of Garner Insights, the enterprise data platform that we sell to healthcare providers. Our members and customers rely on this work to answer hard questions: Which doctors are truly high quality? What will their care cost? Which providers are driving poor outcomes in a given network? The quality of those answers is determined by the algorithms your team builds.  This is not an Applied Science function charged with advising the business; you will own the business. You will set the technical direction, build and develop a world-class team, own the production systems end-to-end, and answer for both the outcomes and the commercial results they drive. You’ll manage a team of applied scientists, clinical researchers, and product managers, and will work closely with data and ML Ops engineering.  The closest analog outside healthcare is the head of a quantitative trading group at a top hedge fund — someone who has done the work themselves and has since built and led the organization that does it at scale. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday.  What you will do: Lead a team of applied scientists and clinical researchers to build and evolve Garner’s foundational doctor-ranking and other doctor categorization algorithms  Own the roadmap and standards for Garner Health Intelligence Build, lead, and develop the GHI organization across Applied Science, Clinical Research, and Garner Insights, hiring and growing both ICs and managers Be accountable for the business results of GHI, including the success of our rankings in improving healthcare cost and quality, as well as the commercial performance of Garner Insights Stay close enough to the work to personally shape the hardest, most ambiguous problems — setting the objective functions, tradeoffs, and decision frameworks the team builds against Decide where to invest, from machine learning to optimization to heuristics to simpler methods, and direct the team's effort to where it moves the company most Partner with the executive team and cross-functional leaders across Product, Engineering, Clinical, and Commercial to align GHI with company strategy Build a deep understanding of the healthcare economy and Garner's place in it, and represent GHI at the highest level inside and outside the company The ideal candidate has: 7+ years of experience in applied science, quantitative research, machine learning, or a closely related field, including significant time spent personally building algorithmic systems; a relevant advanced
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