About the job
At Garner Health, our mission is to revolutionize the healthcare economy, ensuring high-quality and affordable care for everyone.
We are fundamentally rethinking the healthcare landscape in the U. S. by collaborating with employers to redesign healthcare benefits using transparent incentives and potent, data-driven insights. Our strategy empowers employees to access higher-quality, more affordable care, creating a system that benefits all stakeholders. Patients attain better health outcomes, employers utilize healthcare funds more effectively, and physicians are incentivized to provide outstanding care rather than simply performing more procedures.
Garner is recognized as one of the fastest-growing healthcare technology firms in the nation. Our solutions are trusted by the most discerning employers and providers in the industry, and we are assembling a team of talented, mission-driven professionals dedicated to making a substantial impact on healthcare at scale.
About the Role:
We are in search of an outstanding Applied Scientist to enhance our core search systems that power our product, particularly focusing on provider selection and recommendation algorithms. You will be instrumental in shaping how Garner identifies high-value providers for our members by developing evaluation frameworks, objective functions, and algorithms that optimize our search experience.
This position is perfect for someone who thrives on tackling ambiguous, high-impact challenges and can convert complex real-world constraints into robust algorithmic solutions. You will collaborate closely with Product and Engineering teams to design and implement production algorithms that balance provider quality, affordability, and accessibility.
Where You Will Work:
This role is based in our New York City office. You must be willing to work in the office three days a week on Tuesday, Wednesday, and Thursday.
Your Responsibilities:
- Develop and maintain the algorithms that drive Garner’s search functionalities.
- Create objective functions, scoring frameworks, and decision logic that balance competing priorities like quality, cost, and access.
- Transform ambiguous product and business challenges into rigorous algorithmic methodologies suitable for production deployment.
- Collaborate with engineers to scale production systems effectively.
- Assess when applied machine learning, optimization, heuristics, or rule-based methods are appropriate for a given challenge.
- Gain a deep understanding of the healthcare economy.

