Data Scientist Ii, Real World Evidence (rwe), Pharma R&d

Tempus AI · Vertical AI · Boston, MA +2

Data Scientist II role focused on Real World Evidence (RWE) in Pharma R&D, leveraging Tempus' multimodal platform. Responsibilities include leading observational studies, deriving insights from clinical data, implementing advanced statistical methods, and incorporating LLMs and agentic workflows to accelerate research and analysis. The role requires strong proficiency in R, SQL, machine learning, and communication with stakeholders.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. Incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation.
  5. 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.

Skills

Required

  • PhD or Master's degree and 2+ years of additional work experience
  • Proficiency with observational real-world healthcare data
  • 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., 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
  • strong project management skills

Nice to have

  • 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)

What the JD emphasized

  • lead observational studies
  • implement advanced statistical methods
  • leverage cutting-edge AI tools
  • derive complex real-world endpoints
  • methodological advancements
  • incorporate LLMs, agentic workflows and other AI tools
  • Proficiency applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor) and agentic frameworks

Other signals

  • incorporate LLMs, agentic workflows and other AI tools into day-to-day workflows
  • leverage cutting-edge AI tools to scale tasks and augment insights
  • Proficiency applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor) and agentic frameworks to support data analysis, code review, or scientific documentation workflows