Data Scientist II, Real World Evidence, Life Sciences R&D Full-time NEW

RWE Data Scientist II

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

The Real World Evidence (RWE) group within the Life Sciences R&D team at Tempus works with major pharmaceutical partners to provide best-in-class data, analysis, and methodological guidance for Tempus's real-world data offering.

We are seeking a highly motivated and solutions-oriented RWE Data Scientist II with experience and interest in oncology and epidemiological study design to join our team. This role requires the ability to lead observational studies, derive insights from complex real-world clinical data, implement advanced statistical methods, and leverage cutting-edge AI tools to scale tasks and augment insights.

Key Responsibilities

• Pharma Collaboration & Strategy: Partner with pharmaceutical collaborators to independently execute robust RWE research plans that leverage the Tempus multimodal platform to address key questions in trial design and outcomes research.
• Real World Data Expertise: Lead the derivation of complex real-world endpoints using extensive coding, demonstrating deep comprehension of Tempus clinical and molecular data structures and complexity, while also serving as an expert on the methodological nuances and limitations of real-world data.
• Methodology & Platform Contribution: Stay up-to-date on methodological advancements in real-world studies (e.g., causal inference, survival analysis) and oncology guidelines (NCCN and ongoing clinical trials) to contribute to reusable code, internal packages, and best practices that can be applied across multiple collaborations.
• AI & LLM Innovation: Incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation.
• Scientific Interpretation & Communication: Interpret results of RWE analyses to draw appropriate inferences based on study design/statistical methods, while also evaluating study limitations. Communicate complex methods and results clearly to both technical and non-technical stakeholders. Prepare and present internal reports, external-facing deliverables, and, where appropriate, manuscripts or conference materials.
• Cross-Functional Collaboration: Collaborate with internal product, oncology, and clinical abstraction, and real-world data science teams to continually enhance Tempus data quality, products, and analytical best practices.

Minimum Qualifications

• Education: Education in epidemiology, biostatistics, data science, public health, or a related field, to the level of either:
• PhD
• Master's degree and 2+ years of additional work experience
• Technical and Statistical Proficiency
• Proficiency with observational real-world healthcare data, including analytical experience with time-to-event methodologies (survival analysis).
• Proven expertise in executing RWD analytical studies.
• Proficient in using R and SQL, especially statistical tools and packages.
• Adherence to good software engineering practices (version control, modular code, documentation).
• Communication & Client Focus: Demonstrated experience interfacing with clients, showcasing adeptness in presenting and tailoring messaging to a variety of stakeholders.
• Soft Skills: Excellent written and verbal communication skills with strong project management skills. Ability to thrive in a fast-paced, dynamic environment working with multi-disciplinary scientists on complex problems.

Preferred Skillsets

• Experience working with Pharma or drug development.
• Experience in clinical trial design (particularly Phase II-III) in the clinical development space.
• Analytical proficiency with claims, EHR, or registry data.
• Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work.
• Knowledge of oncology guidelines (e.g., NCCN).
• Experience with biomarker or molecular data (e.g., genomics).
• Experience with cloud platforms such as AWS and/or BigQuery and/or Google Cloud Platform (GCP).