About the role:

We are seeking an exceptional Staff Applied Scientist 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.

This is a player-coach role. You will be a hands-on-keys Applied Scientist, while also leading a small team that helps you deliver on your roadmap. Your time will be split between your own technical work and working with your team to shape how they approach their problems. This role is a good fit for someone who wants to develop their management toolkit while continuing to work closely on their own technical work, and it can lead either towards management or a deeper senior IC track.
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
• Lead a small team - set their technical direction, unblock them when they're stuck, and share accountability for their growth and the quality of what they ship
• 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 interest in mentoring or technically leading other Scientists - formal management experience is welcome, but not required
• 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

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 work on high-impact problems in healthcare - shaping how members find better care through algorithmic systems that directly influence healthcare outcomes, and helping the scientists around you do the same.
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.

Fraud and Security Notice:

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