Manager, Applied Science

Where you will work:
This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday.

What you will do:
Lead a team of Applied Scientists — hire, coach, and develop them, hold the bar on both rigor and speed, and help raise that bar across Applied Science
Own your team's roadmap and delivery: what gets built, in what order, and whether it ships
Set technical direction, and get in the weeds to do it — stay close enough to the code and the data to call the approach yourself, whether that's machine learning, optimization, a heuristic, or a simple rule, and set the standard for how your team makes that call
Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship
Put your own hands on the problems where your judgment matters most — the highest-stakes, least-defined work — and deliver the algorithmic step-changes that move metrics like total-cost-of-care savings, steerage, and member engagement
Turn ambiguous business goals into a clear problem set alongside Research, Product, Engineering, and business stakeholders, and represent your team's work to senior leadership
Set the quality bar: review your team's work with rigor, and define the metrics that decide whether a solution is working
Build a deep understanding of the healthcare economy and Garner's place in it
To make the role concrete, here are three 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.
AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely.
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.

The ideal candidate has:
6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred
Deep technical credibility, with the range and judgment to lead scientists who are experts in their own right, and the willingness to stay hands-on
Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them
A bias toward action, quickly translating ideas into working prototypes to test approaches
Strong communication skills, including at the executive level, with a track record of driving alignment across teams and functions
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
Technologies we use:
Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment.