Senior Computational Biologist – AI-Driven Drug Discovery

Tempus; Chicago (IL); On-site; USD 100,000 - 160,000; Full time

Benefits offered by this job

Incentive compensation

Restricted stock units

Medical benefits

Job summary

Tempus is seeking an experienced Senior Scientist to join the Computational Biology, Pharma R&D team. The role focuses on integrating large-scale molecular and clinical data to generate actionable insights for drug discovery and development, while building next-generation research tools.

The ideal candidate will lead projects, collaborate across research, clinical, data science and engineering, and mentor junior scientists.

Qualifications

• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or related field (or Masters with 4+ years).
• Plus 2+ years of industry or post-doctoral experience.
• Track record of success: peer reviewed publications.
• Proficiency in R and/or Python with bioinformatics libraries.
• Experience applying ML, LLM-based coding assistants (Copilot, Cursor) and agentic frameworks.
• Good software engineering practices: version control, modular code, documentation.
• Strong statistics and data analysis, study design and interpretation of real-world data.
• Excellent written and verbal communication skills and collaboration.

Responsibilities

• Pharma collaboration: partner with pharma teams on discovery and biomarker development.
• Integrate genomic, transcriptomic, imaging, and clinical data to derive insights.
• Apply AI tools and LLMs to automate workflows and prototype predictive models.
• Develop methods for real-world and ’omic data analyses (survival, causal inference).
• Contribute reusable code and best practices across programs.

Skills

R

Python

Machine learning

LLM

Copilot/Cursor

Education

PhD in Computational Biology or related field

Masters with 4+ years experience

Tools

Copilot

Cursor

Job description

Tempus is seeking an experienced Senior Scientist to join the Computational Biology, Pharma R&D team. The role focuses on integrating large-scale molecular and clinical data to generate actionable insights for drug discovery and development, while building next-generation research tools.

The ideal candidate will lead projects, collaborate across research, clinical, data science and engineering, and mentor junior scientists.