Manager, Applied Science

About the role:

We are seeking an exceptional Manager, Applied Science to build and lead a team at the core of Garner's 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 a hands-on management role. You are responsible for the overall technical direction of your squad, building alongside your team of Applied Scientists to solve some of the hardest problems at the company.

This is not a dashboards or descriptive-analytics team. Your team owns 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 leading a research pod at a top hedge fund.

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.

This is a unique opportunity to build and lead the team behind Garner's algorithmic systems — shaping how members find better care, and how applied science gets done at Garner, while staying close to the work yourself.

Compensation Transparency:

The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.