Applied Scientist II

Garner Health•New York City, NY

7d•Hybrid

About The Position

Garner is seeking an exceptional Applied Scientist II to join our Applied Science team. Garner is hiring Applied Scientists to design and ship the algorithmic systems at the core of our product. 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.

Requirements

2+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent advanced degree
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 command of the methods your work calls for and the data your models depend on
Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
Strong communication skills, with the ability to present your work clearly to senior stakeholders
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

Responsibilities

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