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HomeCompaniesEverlywellApplied Scientist — ML, Experimentation & Decision Systems

Applied Scientist — ML, Experimentation & Decision Systems

Everlywell · Austin, TX · Hybrid · Active · Lever

Job facts

FieldValue
CompanyEverlywell
TitleApplied Scientist — ML, Experimentation & Decision Systems
Normalized title-
Department / teamTechnology / Engineering
LocationAustin, TX, United States
Work modelHybrid / Hybrid
Employment typeFull Time
Salary-
Statusactive
ATS providerLever
Posted / first seen2026-03-06 / 2026-05-29
Changed / last seen2026-05-29 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Everlywell.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Lever.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in Austin.Open
Department jobsActive postings in Technology.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyEverlywell
Sourcea2861e73-3940-4c94-8300-a9e1b19f29c8
ATS providerLever

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 2025, an estimated 1 in 94 U.S. adults received an Everlywell test, solidifying our spot as the #1 at-home testing company in the country. 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. Security Notice: Everlywell never requests fees, payment, or banking information at any stage of the recruitment process. Official communications and interview invitations will only come from verified email addresses ending in @everlywell.com or @everlyhealth.com. To ensure your application is secure, always apply directly through our official careers page at https://www.everlywell.com/careers/. 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

Full job record

Job ID0ac80df7c3e6e31f5750e8adf62ee39730bb2518
Org IDe9dd650a-24c7-475e-903c-c1eefe2fbf94
Source IDa2861e73-3940-4c94-8300-a9e1b19f29c8
Board IDa2861e73-3940-4c94-8300-a9e1b19f29c8
Providerlever
Provider Job Key948104c7-330a-4674-b1a3-2d5b57d43b1e
TitleApplied Scientist — ML, Experimentation & Decision Systems
Normalized Title
Statusactive
Activeyes
Location TextAustin, TX
DepartmentTechnology
TeamEngineering
Employment TypeFull-Time
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionTX
CityAustin
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.lever.co/everlywell/948104c7-330a-4674-b1a3-2d5b57d43b1e
Apply URLhttps://jobs.lever.co/everlywell/948104c7-330a-4674-b1a3-2d5b57d43b1e/apply
First Seen At2026-05-29 07:01:32Z
Last Seen At2026-06-06 07:57:01Z
Last Checked At2026-06-06 07:57:01Z
Last Changed At2026-05-29 07:01:32Z
Inactive At
Source Posted At2026-03-06 21:02:45Z
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=lever/board=everlywell/date=2026-06-06/2026-06-06T07-57-01-417Z-2b2dabd0a567121f82054eedfc2dc95977af50a3cf117e875d243b383467a7e2.json
Event Fields
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  "last_changed_at": "2026-05-29T07:01:32.227Z",
  "active_status": "active"
}
Parsed Structured
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    "city": "Austin",
    "region": "TX",
    "country": "United States",
    "is_remote": false,
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}
Extensions
{}
Native Structured
{
  "lists": [
    {
      "text": "Responsibilities",
      "content": "\n<li>Build and improve ML models used in engagement and operational workflows</li>\n<li>Develop models for prediction, prioritization, uplift, and related decisioning use cases</li>\n<li>Define and monitor model performance, business impact, and system health</li>\n<li>Design and analyze A/B tests and other measurement approaches to evaluate incremental impact</li>\n<li>Partner with stakeholders to define success metrics and turn findings into decisions</li>\n<li>Support production rollout and ongoing monitoring with engineering teams</li>\n<li>Help evaluate AI- and LLM-powered workflows used in production settings</li>\n\n<div>&nbsp;</div>"
    },
    {
      "text": "Skills & Abilities Required:",
      "content": "\n<li>5+ years in Applied Science, Data Science, ML, Decision Science, or similar roles</li>\n<li>Strong hands-on experience training, evaluating, and improving ML models</li>\n<li>Strong experience designing and analyzing A/B tests</li>\n<li>Strong Python and SQL skills</li>\n<li>Experience measuring model, program, or product performance in production</li>\n<li>Ability to work cross-functionally and communicate clearly with stakeholders</li>\n<li>PreferredExperience in experimentation platforms, growth or lifecycle modeling, or ML-driven decision systems</li>\n<li>Experience with causal inference or uplift modeling</li>\n<li>Experience with LLMs, AI agents, or automated workflows in production</li>\n<li>Experience in healthcare or regulated environments</li>\n<li>Snowflake, Python, dbt, Airflow, model registry systems, GitLab</li>\n"
    }
  ],
  "country": "US",
  "createdAt": 1772830965111,
  "updatedAt": null,
  "categories": {
    "team": "Engineering",
    "location": "Austin, TX",
    "commitment": "Full-Time",
    "department": "Technology",
    "allLocations": [
      "Austin, TX"
    ]
  },
  "salaryRange": null,
  "workplaceType": "hybrid"
}
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