Senior Data Scientist I, Real World Evidence (RWE), Life Sciences R&D

Key Responsibilities

• Pharma Collaboration & Strategy: Lead the design and execute delivery of RWE analyses for key pharma clients. Translate complex drug development questions into actionable research plans that use Tempus data for 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 serving as an expert on methodological nuances and limitations of real-world data.
• Methodology & Platform Contribution: Build technical standards for the team, staying up-to-date on methodological advancements in real-world studies (e.g., causal inference, survival analysis) and oncology guidelines (NCCN and ongoing clinical trials). Contribute to reusable code and internal packages.
• 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 evaluating study limitations. Communicate complex methods and results clearly to technical and non-technical stakeholders. Prepare and present internal reports, external-facing deliverables, and manuscripts or conference materials.
• Cross-Functional Collaboration: Collaborate with internal product, oncology, clinical abstraction, and real-world data science teams to enhance Tempus data quality, products, and analytical best practice. Proactively identify gaps in current products and ensure customer feedback is represented in development of new products.
• Oncology & RWE Domain Expertise: Grow and maintain deep expertise in oncology clinical guidelines (e.g., NCCN) and emerging RWE methodologies.

Minimum Qualifications

• Education: PhD and 2+ years of additional work experience OR Master's degree and 4+ years of additional work experience in epidemiology, biostatistics, data science, public health, or a related field.
• Expert-level 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).
• Experience with code review.
• 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.

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 building, fine-tuning, or configuring LLM-based tools and agentic workflows specifically for scientific discovery.
• Knowledge of oncology guidelines (e.g., NCCN).
• Significant experience analyzing biomarker, genomic, or other high-dimensional molecular data alongside clinical datasets.
• Experience with cloud platforms such as AWS and/or BigQuery and/or Google Cloud Platform (GCP).