Senior Applied Scientist

Garner’s mission is to transform the healthcare economy, delivering high-quality and affordable care for all.

We are fundamentally reimagining how healthcare works in the U.S. by partnering with employers to redesign healthcare benefits using clear incentives and powerful, data-driven insights. Our approach guides employees to higher-quality, lower-cost care, creating a system that works better for everyone. Patients achieve better health outcomes, employers spend healthcare dollars more effectively, and physicians are rewarded for delivering exceptional care rather than performing more procedures.

Garner is one of the fastest-growing healthcare technology companies in the country. Our products are trusted by the most sophisticated employers and providers in the industry, and we are building a team of talented, mission-driven individuals who are motivated to make a meaningful impact on healthcare at scale.

About the Role:

We are seeking an exceptional Senior Applied Scientist to join our Applied Science team. In this role, you will design, develop, and deploy the algorithmic systems that power Garner's products and drive meaningful impact for our members. 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.

What you will do:

Own the most ambiguous, high-stakes problems on the team end-to-end, and serve as a technical resource others rely on

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

Find novel ways to frame and solve the team's hardest problems, proving out approaches that others build on

Set the bar for quality by reviewing others' work with rigor, and build the standards and evaluation tooling the team relies on

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:

4+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 2+ 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

Deep technical range, with fluency across Garner's data and a habit of staying current with advances in the field

Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem

Strong communication skills and the ability to synthesize complex algorithmic ideas for senior and external stakeholders, and to secure buy-in for cross-team work

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 join a fast-growing company in a transformative role, helping shape the future of healthcare.

Compensation Transparency:

The target base comp range for this position is $236,000 – $260,000 . Individual compensation for this role will depend on various factors, including qu…