Data Scientist II, Real World Evidence, Life Sciences R&D

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 or 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. Proficiency applying machine learning, LLM-based coding assistants (e.g., Claude Code, Copilot, Cursor) and agentic frameworks to support data analysis, code review, or scientific documentation workflows. 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).