Data Scientist II, 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. Requirements 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. #J-18808-Ljbffr Jobtailor