Applied Scientist — ML, Experimentation & Decision Systems bei Everlywell
Everlywell · Austin, Vereinigte Staaten Von Amerika · Hybrid
- Optionales Büro in Austin
Description
Everlywell is a digital health company pioneering the next generation of biomarker intelligence—combining AI-powered technology with human insight to deliver personalized, actionable health answers. We transform complex biomarker data into life-changing insights—seamlessly integrating advanced diagnostics, virtual care, and patient engagement to reshape how and where health happens.
Over the past decade, Everlywell has delivered close to 1 billion personalized health insights, transforming care for 60 million people and powering hundreds of enterprise partners. In 2024 alone, an estimated 1 in 86 U.S. households received an Everlywell test, solidifying our spot as the #1 at-home testing brand in the country. And we’re just getting started. Fueled by AI and built for scale, we’re breaking down barriers, closing care gaps, and unlocking a more connected healthcare experience that is smarter, faster, and more personalized.
Everlywell operates large-scale health engagement programs that help health plan members complete important care actions — from returning diagnostic kits to accessing preventive and virtual care.
We’re hiring an Applied Scientist to build and measure the ML systems that power these programs. This role is focused on machine learning, experimentation, and production measurement. You’ll train models, evaluate performance, design A/B tests, and work with engineering and business stakeholders to improve real-world outcomes.
This is a high-impact opportunity to apply ML and experimentation skills to systems that influence real member outcomes at scale. You’ll work on practical, production-facing problems with clear business value, strong cross-functional visibility, and room to help shape how Everlywell uses both ML and AI in operational workflows.If you’re excited by hands-on modeling, rigorous experimentation, and building systems that improve decisions in the real world, we’d love to hear from you.
