Note: The job is a remote job and is open to candidates in USA. Tempus AI uses a proprietary platform to connect real-world evidence and deliver actionable insights for clinical care. The Data Scientist II, Outcomes Research role leads HEOR and real-world evidence projects, collaborates with internal and external stakeholders, develops analytical infrastructure, and applies epidemiologic and health economic methodologies to healthcare data.
Responsibilities
- 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
- Represent the Outcomes Research function and collaborate with internal and external stakeholders in the design, analysis, interpretation, and publication of real-world studies
- Work on complex problems, exercising judgment in selecting and adapting appropriate epidemiologic and health economic methodologies
- Partner with interdisciplinary groups of scientists, engineers, and product developers to translate research into clinically actionable insights for our clients
- Stay current with the latest methodological advances in RWE, including causal inference and pharmacoepidemiologic methods
- Build analytical infrastructure, including reusable code, templates, and workflows that improve speed and quality across engagements
- Comply with all applicable regulations, Tempus data governance, and company procedures related to real-world data use and reporting
Skills
- Advanced degree (Master's with 2+ years experience or equivalent) in data science, bioinformatics, biostatistics, epidemiology, immunology, public health, or related quantitative field
- Demonstrated computational skills using R and SQL, specifically applied to large-scale healthcare datasets
- Strong data manipulation and analytical skills tailored to observational/real-world data
- Deep familiarity with HEOR and RWE methodologies, including approaches to address confounding (e.g., propensity score matching, weighting, inverse probability of treatment weighting)
- Experience analyzing large, complex real-world datasets, including administrative claims, electronic health records (EHR), and/or clinico-genomic databases
- Strong communication and presentation skills with the ability to translate complex methodologies and findings for non-technical stakeholders
- Self-driven mindset with demonstrated ability to tackle ambiguous problems and work effectively in interdisciplinary teams
- Experience with time-to-event analysis and survival methodologies
- Experience working in oncology and/or analyzing outcomes related to cancer genetics, immunology, or molecular biology
- Collaborative working style, eagerness to learn, and high-integrity work ethic
- Sharp attention to detail and a passion for delivering high-quality, timely analytics
- Ability to draw appropriate inferences based on study design and explicitly assess and communicate study limitations
- Experience with version control (e.g., Git) and software testing or validation processes
- Experience working in oncology Phase II-IV clinical trials and/or experience with the analysis of RWD and/or HEOR studies (e.g. using claims, EHR or registry data sources)
- Hands-on experience contributing to regulatory submissions to the FDA or other health authorities
- Experience supporting data science teams in model building and validation, including feature engineering and performance assessment
- Client-facing or consulting experience and comfort presenting results and recommendations to external stakeholders
Benefits
- Incentive compensation may be available depending on the position.
- Restricted stock units may be available depending on the position.
- Medical benefits may be available depending on the position.
- Other benefits may be available depending on the position.
Company Overview
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