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

Tempus AI•Boston, MA

4d•Remote

About The Position

Tempus is seeking a highly motivated and solutions-oriented Real World Evidence (RWE) Data Scientist II to join the Life Sciences R&D team. This role focuses on leveraging Tempus' proprietary platform, which connects real-world evidence to provide actionable insights for physicians. The RWE group collaborates with pharmaceutical partners, offering data, analysis, and methodological guidance. The ideal candidate will have experience and interest in oncology and epidemiological study design, with the ability to lead observational studies, derive insights from complex real-world clinical data, implement advanced statistical methods, and utilize AI tools. The role involves partnering with pharmaceutical collaborators, leading the derivation of complex real-world endpoints, contributing to methodological advancements, incorporating AI/LLMs into workflows, interpreting and communicating RWE analysis results, and collaborating with internal teams to enhance data quality and analytical practices.

Requirements

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.
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).
Demonstrated experience interfacing with clients, showcasing adeptness in presenting and tailoring messaging to a variety of stakeholders.
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.

Nice To Haves

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).

Responsibilities

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.
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.
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.
Incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation.
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.
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.