Staff Applied Scientist

Garner’s mission is to transform the healthcare economy, delivering high-quality and affordable care for all. S.

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 Staff Applied Researcher to join our Applied Science team. You will be responsible for building the algorithmic systems that power Garner — determining how we evaluate providers, make recommendations, and optimize for outcomes across cost, quality, and access. You will be responsible for turning ambiguous, real-world problems into systems that deliver measurable impact, defining the objective functions, metrics, and logic that drive our product.

You will own these systems end-to-end, from problem definition through production and ongoing performance. 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

: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Review applied science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions.

AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model.

Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care.