Applied Scientist

What you’ll be part of

Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster.

The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.

Who we want

This is a role for someone who wants to work on a hard problem, deliver real impact and be pushed to be the best version of themselves. As an Associate Applied Scientist, you’ll be embedded in the Applied Science team. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them.

This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund.

What you will do

• Ship production algorithmic systems end-to-end, from problem framing to launch to iteration
• 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
• Bring new ideas to your work, experimenting with approaches beyond the obvious to get better results
• Produce rigorous, well-validated work others can rely on, and review your peers' code and analyses
• Build a deep understanding of the healthcare economy and Garner's place in it