Applied Scientist III

Garner Health•New York, NY

8d•$236,000 - $260,000

About The Position

Garner is on a mission to transform the U.S. healthcare system by partnering with employers to redesign how healthcare works. We apply proprietary clinical metrics to a large patient dataset to identify the best-performing doctors and use incentives to guide members to higher-quality care, resulting in better health outcomes and lower costs. We have experienced significant growth and are seeking to scale our impact further and faster using AI. This role is within the Applied Science team, responsible for designing, developing, and deploying the algorithmic systems that power Garner's products. Members rely on these algorithms for critical questions about healthcare choices. This is a hands-on role focused on production systems, from problem framing and objective definition to implementation and improvement, similar to a quantitative researcher at a top hedge fund.

Requirements

4+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 2+ years of industry experience with a relevant advanced degree, PhDs preferred
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
Deep technical range, with fluency across Garner's data and a habit of staying current with advances in the field
Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
Strong communication skills and the ability to synthesize complex algorithmic ideas for senior and external stakeholders, and to secure buy-in for cross-team work
A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Nice To Haves

Python
SQL
AWS
Snowflake
pandas
XGBoost
PyTorch
HuggingFace
modern LLM tooling and eval frameworks

Responsibilities

Own the most ambiguous, high-stakes problems on the team end-to-end, and serve as a technical resource others rely on
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
Find novel ways to frame and solve the team's hardest problems, proving out approaches that others build on
Set the bar for quality by reviewing others' work with rigor, and build the standards and evaluation tooling the team relies on
Build a deep understanding of the healthcare economy and Garner's place in it