Data Scientist Ii, Outcomes Research

Tempus AI Tempus AI · Vertical AI · Chicago, IL +2

This role focuses on outcomes research using real-world data (RWD) in healthcare, partnering with external organizations to provide data, analysis, and methodological guidance. The Data Scientist will lead and execute HEOR and RWE projects, collaborate with stakeholders, and build analytical infrastructure. While AI is mentioned as impacting clinical care, the core of this role is in traditional pharmacoepidemiologic and HEOR study design and analysis, not direct AI/ML model development.

What you'd actually do

  1. Lead and execute HEOR and real-world evidence (RWE) projects (e.g., outcomes analysis, treatment patterns, healthcare resource utilization) with external Pharma, academic, and other partners.
  2. Represent the Outcomes Research function and collaborate with internal and external stakeholders in the design, analysis, interpretation, and publication of real-world studies.
  3. Work on complex problems, exercising judgment in selecting and adapting appropriate epidemiologic and health economic methodologies.
  4. Partner with interdisciplinary groups of scientists, engineers, and product developers to translate research into clinically actionable insights for our clients.
  5. Stay current with the latest methodological advances in RWE, including causal inference and pharmacoepidemiologic methods.

Skills

Required

  • R
  • SQL
  • large-scale healthcare datasets
  • Master’s degree (or equivalent experience)

Nice to have

  • observational/real-world data analysis
  • confounding adjustment methods (propensity score matching, weighting, IPW)
  • large, complex real-world datasets (claims, EHR, clinico-genomic)
  • time-to-event analysis
  • survival methodologies
  • oncology experience
  • cancer genetics/immunology/molecular biology analysis
  • version control (Git)
  • software testing/validation
  • oncology Phase II-IV clinical trials
  • claims/EHR/registry data analysis
  • regulatory submissions (FDA)
  • model building and validation support
  • feature engineering
  • performance assessment
  • client-facing experience
  • consulting experience

What the JD emphasized

  • external Pharma, biotech, and academic institutions
  • pharmacoepidemiologic and health economic outcomes research (HEOR) studies
  • HEOR and real-world evidence (RWE) projects
  • epidemiologic and health economic methodologies
  • methodological advances in RWE
  • causal inference and pharmacoepidemiologic methods