Sr. Data Scientist, Outcomes Research

Tempus AI•Chicago, IL

About The Position

Tempus is seeking a highly motivated and capable Senior Data Scientist to join its Outcomes Research team. The team partners with external pharmaceutical, biotechnology, and academic institutions to provide data, analysis, and methodological guidance for Tempus’s real-world data offering. The role focuses on designing and analyzing pharmacoepidemiologic and health economic outcomes research studies and delivering actionable insights from real-world evidence. The Senior Data Scientist will lead external real-world evidence and health economics and outcomes research projects, collaborate with internal and external stakeholders on study design and publication, and apply appropriate epidemiologic and health economic methodologies to complex problems. The role also works with interdisciplinary teams to translate research into clinically actionable insights, advances reusable analytical infrastructure, keeps current with methodological developments, and follows applicable regulations and Tempus procedures for real-world data use and reporting. The position may be performed remotely in the U.S. or from New York City or San Francisco. The stated salary ranges are $100,000–$160,000 for Remote US and $115,000–$175,000 for NYC/SF; the posting notes that ranges may vary by location and that actual compensation depends on qualifications and experience. Tempus offers benefits that may include incentive compensation, restricted stock units, medical benefits, and other benefits depending on the position.

Requirements

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

Nice To Haves

Strong data manipulation and analytical skills tailored to observational and real-world data.
Deep familiarity with HEOR and RWE methodologies, including methods to address confounding such as propensity score matching, weighting, and inverse probability of treatment weighting.
Experience analyzing large, complex real-world datasets, such as administrative claims, electronic health records, and/or clinico-genomic databases.
Strong communication and presentation skills, including the ability to explain complex methodologies and findings to non-technical stakeholders.
Self-driven approach, ability to tackle ambiguous problems, and effectiveness in interdisciplinary teams.
Experience with time-to-event analysis and survival methodologies.
Experience 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.
Attention to detail and commitment to delivering high-quality, timely analytics.
Ability to draw appropriate inferences from study design and explicitly assess and communicate study limitations.
Experience with version control such as Git and software testing or validation processes.
Experience working in oncology Phase II–IV clinical trials and/or analyzing RWD studies using claims, EHR, or registry 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.

Responsibilities

Lead and execute HEOR and real-world evidence projects, including outcomes analyses, treatment patterns, and healthcare resource utilization, with external pharmaceutical, academic, and other partners.
Represent the Outcomes Research function and collaborate with internal and external stakeholders on the design, analysis, interpretation, and publication of real-world studies.
Address complex problems by 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 clients.
Stay current with methodological advances in real-world evidence, including causal inference and pharmacoepidemiologic methods.
Build reusable analytical infrastructure, including code, templates, and workflows, to improve the speed and quality of engagements.
Comply with applicable regulations, Tempus data governance, and company procedures related to real-world data use and reporting.