Applied Scientist — ML, Experimentation & Decision Systems

Austin, TX - USA

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

Everlywell is a digital health company pioneering the next generation of biomarker intelligencecombining AI-powered technology with human insight to deliver personalized actionable health answers. We transform complex biomarker data into life-changing insightsseamlessly 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 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 were just getting started. Fueled by AI and built for scale were 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.

Were 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. Youll 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. Youll 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 youre excited by hands-on modeling rigorous experimentation and building systems that improve decisions in the real world wed love to hear from you.

Responsibilities

Build and improve ML models used in engagement and operational workflows
Develop models for prediction prioritization uplift and related decisioning use cases
Define and monitor model performance business impact and system health
Design and analyze A/B tests and other measurement approaches to evaluate incremental impact
Partner with stakeholders to define success metrics and turn findings into decisions
Support production rollout and ongoing monitoring with engineering teams
Help evaluate AI- and LLM-powered workflows used in production settings

Skills & Abilities Required:

5 years in Applied Science Data Science ML Decision Science or similar roles
Strong hands-on experience training evaluating and improving ML models
Strong experience designing and analyzing A/B tests
Strong Python and SQL skills
Experience measuring model program or product performance in production
Ability to work cross-functionally and communicate clearly with stakeholders
PreferredExperience in experimentation platforms growth or lifecycle modeling or ML-driven decision systems
Experience with causal inference or uplift modeling
Experience with LLMs AI agents or automated workflows in production
Experience in healthcare or regulated environments
Snowflake Python dbt Airflow model registry systems GitLab

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

Required Experience:

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.

Responsibilities

Build and improve ML models used in engagement and operational workflows
Develop models for prediction prioritization uplift and related decisioning use cases
Define and monitor model performance business impact and system health
Design and analyze A/B tests and other measurement approaches to evaluate incremental impact
Partner with stakeholders to define success metrics and turn findings into decisions
Support production rollout and ongoing monitoring with engineering teams
Help evaluate AI- and LLM-powered workflows used in production settings

Skills & Abilities Required:

5 years in Applied Science Data Science ML Decision Science or similar roles
Strong hands-on experience training evaluating and improving ML models
Strong experience designing and analyzing A/B tests
Strong Python and SQL skills
Experience measuring model program or product performance in production
Ability to work cross-functionally and communicate clearly with stakeholders
PreferredExperience in experimentation platforms growth or lifecycle modeling or ML-driven decision systems
Experience with causal inference or uplift modeling
Experience with LLMs AI agents or automated workflows in production
Experience in healthcare or regulated environments
Snowflake Python dbt Airflow model registry systems GitLab

Required Experience: