Senior Data Scientist – Real World Evidence & AI

Tempus AI; New York (NY); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Full benefits package

Equity / RSUs may be offered

Job summary

Tempus is seeking a senior analytics scientist to lead RWE analyses for pharma clients, translating drug development questions into actionable research plans using Tempus data. You will shape endpoints, apply advanced survival analyses, and contribute reusable code and standards for the team.

You will work with cross-functional teams to communicate complex results to both technical and non-technical stakeholders, and explore LLM-enabled coding assistants to accelerate discovery and documentation.

Qualifications

• Experience with real-world healthcare data and time-to-event analyses.
• Proven ability to run RWD analytical studies with rigor.
• Experience interfacing with pharma clients and presenting results.

Responsibilities

• Lead design and delivery of RWE analyses for pharma clients.
• Derive complex endpoints using Tempus clinical and molecular data.
• Develop methodological standards and reusable code.
• Integrate AI tools and agentic workflows into data workflows.
• Interpret results and communicate limitations clearly to stakeholders.
• Collaborate across product, oncology, and data science teams.

Skills

Observational real-world data

Survival analysis

R & SQL proficiency

LLM-based coding assistants

Software engineering practices

Code review

Client communication

Cross-functional collaboration

Education

PhD + 2+ years

Master’s degree + 4+ years

Tools

R

SQL

Claude Code

Copilot

Cursor

Git

Job description

Tempus is seeking a senior analytics scientist to lead RWE analyses for pharma clients, translating drug development questions into actionable research plans using Tempus data. You will shape endpoints, apply advanced survival analyses, and contribute reusable code and standards for the team.

You will work with cross-functional teams to communicate complex results to both technical and non-technical stakeholders, and explore LLM-enabled coding assistants to accelerate discovery and documentation.