Associate 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.

• 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

To make the role concrete, here are two 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.

• 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.

• A Bachelor's or Master's degree (in progress) in Computer Science, Mathematics, Statistics, Economics, or a related STEM field, with an expected graduation date of Fall 2026 or Spring 2027.

• A bias toward action, quickly translating ideas into working prototypes to test approaches.

• Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them.

• A solid