Staff Applied Scientist

About the role

We are seeking an exceptional Staff Applied Researcher to join our Applied Science team. You will be responsible for building the algorithmic systems that power Garner — determining how we evaluate providers, make recommendations, and optimize for outcomes across cost, quality, and access.

You will be responsible for turning ambiguous, real-world problems into systems that deliver measurable impact, defining the objective functions, metrics, and logic that drive our product. You will own these systems end-to-end, from problem definition through production and ongoing performance.

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:

• Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them
• 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, and set the standard for how the team selects and applies these approaches
• Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner
• Review applied science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied
• 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
• 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
• Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound
• Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
• Strong communication skills, including at the executive level, with a track record of driving alignment across an organization
• 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

What you’ll get here

You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission.

At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose.

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.

Why this role

This is a unique opportunity to work on high-impact search problems in healthcare, helping shape how members find better care through algorithmic systems that directly influence healthcare outcomes.

Compensation Transparency:

The target base comp range for this position is $260,000 – $382,000. 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.