Sr. Data Scientist, Outcomes Research

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.

Required Qualifications

• Advanced degree (Master’s with 5+ years experience, PhD with 3+ years experience, or Bachelor’s with 8+ years experience) in data science, bioinformatics, biostatistics, epidemiology, public health, or a related quantitative field.
• Demonstrated computational skills using R and SQL, specifically applied to large-scale healthcare datasets.

Preferred Qualifications

• 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, such as 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.

Nice to Have

• Experience with version control (e.g., Git) and software testing or validation processes.
• Experience working in oncology Phase II–IV clinical trials and/or analysis of RWD studies (e.g., claims, EHR, 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.