Applied Scientist II

Garner Health•New York, NY

8h•$158,000 - $190,000•Hybrid

About The Position

Garner is seeking an exceptional Applied Scientist II to join their Applied Science team. This role involves designing and shipping the algorithmic systems that are core to Garner's product, answering critical questions for members such as which doctor to see, what care will cost, and when to reach out. This is a production-oriented role where individuals will own systems end-to-end, from problem framing and objective function definition to choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping, and improving the systems based on real-world outcomes. The role is compared to a quantitative researcher at a top hedge fund, focusing on applied problem-solving rather than dashboards or descriptive analytics.

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