SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Garner Health is transforming the U.S. healthcare system by partnering with employers to redesign how healthcare works. The company applies 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify best-performing doctors and steer members to higher-quality care. In five years, Garner has helped 2.5 million people access better care and saved $1B in healthcare costs.
As an Applied Scientist III, you will design, develop, and deploy algorithmic systems powering Garner's products. This is a production-focused role, not dashboards or descriptive analytics. You own end-to-end systems: framing problems, defining objective functions, choosing approaches (ML, optimization, heuristics, expert systems, or hybrids), shipping, and improving against real-world outcomes. The role is analogous to a quantitative researcher at a top hedge fund.
Key responsibilities include owning high-stakes, ambiguous problems end-to-end and serving as a technical resource for the team. You will frame messy healthcare and business constraints into clear objectives and decision frameworks, define metrics to judge solution effectiveness, and validate before shipping. You'll choose the right technical approach for each problem based on actual requirements rather than resume preferences. You'll find novel ways to solve the team's hardest problems, set quality bars through rigorous code review, and build evaluation tooling the team relies on.
Near-term roadmap problems include: (1) Provider tiering optimization—building an algorithm that jointly optimizes geographic access and total-cost-of-care savings across the doctor network; (2) AI primary care doctor—fine-tuning and productionizing an LLM-based primary care experience with evaluation harnesses, guardrails, and quality monitoring; (3) Member engagement model—building an ML system that ingests claims data and in-app behavior to choose the right channel and moment for touchpoints (SMS, push, phone, email) to influence member behavior.
Ideal candidates have 4+ years as an Applied Scientist, ML Engineer, or Research Scientist (or 2+ years with an advanced degree; PhDs preferred). You demonstrate bias toward action, translating ideas into working prototypes quickly. You have strong applied problem-solving skills, deep technical range across data, and fluency with modern advances. You show strong judgment choosing between statistical models, heuristics, optimization, and simpler methods. You communicate complex algorithmic ideas clearly to senior and external stakeholders and secure cross-team buy-in. You're mission-driven, operate with urgency, take individual accountability, and value authentic feedback.
Tech stack: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, and modern LLM tooling and evaluation frameworks.
