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Salary: USD 90,000 - 150,000 / annual
Tempus is seeking a Senior Data Scientist to join the Outcomes Research team, which partners with external Pharma, biotech, and academic institutions to deliver best-in-class data, analysis, and methodological guidance for real-world data (RWD) offerings. This role focuses on advancing precision medicine through AI-driven insights that help physicians identify the right treatments for the right patients.
You will lead and execute health economic outcomes research (HEOR) and real-world evidence (RWE) projects, including outcomes analysis, treatment pattern studies, and healthcare resource utilization assessments. You'll represent the Outcomes Research function across internal and external stakeholders, collaborating on the design, analysis, interpretation, and publication of real-world studies. Working with complex problems, you'll exercise judgment in selecting and adapting appropriate epidemiologic and health economic methodologies. You'll partner with interdisciplinary teams of scientists, engineers, and product developers to translate research into clinically actionable insights for clients.
Key responsibilities include staying current with methodological advances in RWE (including causal inference and pharmacoepidemiologic methods), building analytical infrastructure with reusable code and templates to improve speed and quality, and ensuring compliance with all applicable regulations and Tempus data governance procedures.
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 (propensity score matching, weighting, inverse probability of treatment weighting)
- Experience analyzing large, complex real-world datasets (administrative claims, EHR, clinico-genomic databases)
- Strong communication and presentation skills to translate complex methodologies for non-technical stakeholders
- Self-driven mindset with ability to tackle ambiguous problems and work effectively in interdisciplinary teams
- Ability to draw appropriate inferences based on study design and assess/communicate study limitations
PREFERRED QUALIFICATIONS:
- Experience with time-to-event analysis and survival methodologies
- Experience in oncology or analyzing outcomes related to cancer genetics, immunology, or molecular biology
- Version control experience (Git) and software testing/validation processes
- Experience with Phase II-IV clinical trials or RWD/HEOR studies using claims, EHR, or registry data
- Hands-on experience contributing to FDA or other regulatory submissions
- Experience supporting data science teams in model building, validation, feature engineering, and performance assessment
- Client-facing or consulting experience with comfort presenting to external stakeholders
