Scientist II, Computational Biology, Pharma R&D

Tempus AI is a precision medicine and healthcare technology company whose platform connects real-world evidence to provide actionable clinical insights. The Scientist II will support pharmaceutical collaborations by integrating large-scale molecular and clinical datasets, generating insights for drug discovery and development, and building research tools using computational biology, statistics, and AI.

Responsibilities

• Pharma Collaboration & Strategy: Partner with pharmaceutical collaborators to execute computational research plans that leverage the Tempus multimodal platform to address key questions in target discovery, biomarker development, and clinical development
• Computational Analysis & Insight Generation: Perform robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data. Apply appropriate statistical and computational methods to derive insights related to clinical trial design, patient selection, treatment response, resistance mechanisms, and disease biology
• AI & LLM Innovation: Incorporate LLMs, agentic workflows, foundation models and other AI tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation
• Method Development and Platform Contribution: Evaluate, adapt, and implement new methods for the analysis of real-world, clinical, and omics datasets (e.g., survival analysis, causal inference, multimodal integration). Contribute to reusable code, internal packages, and best practices that can be applied across multiple collaborations and programs
• Cross-Functional Collaboration: Work closely with colleagues in Research, Clinical, Data Science, and Engineering to refine analyses and build scalable solutions
• Scientific Communication: 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 that demonstrate the impact of Tempus data and technologies on partner programs

Preferred

• Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work
• Expertise in one or more of the following: Real-world evidence (RWE), survival analysis, causal inference, network/systems biology, or multimodal integration
• A strong history of peer-reviewed publications or conference presentations
• Understanding of the drug development lifecycle, from target discovery to clinical development
• LLM configuration
• adaptation
• LLM tools
• Scientific research support
• Real-world evidence
• Survival analysis
• Causal inference
• Network biology
• Systems biology
• Multimodal data integration
• Peer-reviewed publications
• Drug development lifecycle
• Target discovery
• Clinical development

Benefits

• Incentive compensation, depending on the position
• Restricted stock units, depending on the position
• Medical benefits, depending on the position
• Other benefits, depending on the position

Company

• AI-enabled precision medicine solutions for healthcare
• Chicago, Illinois, USA
• https://www.tempus.com/
• Founded in 2015
• 1001-5000 employees

Qualifications

• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or master's degree with 3+ years of relevant experience)
• Proficiency in R and/or Python, including experience with common computational biology and scientific computing libraries
• Proficiency in using machine learning, LLM-based coding assistants (e.g., Claude Code, Codex), and agentic frameworks for biological/clinical research
• Adherence to good software engineering practices (version control, modular code, documentation)
• Experience working with SQL and large relational databases
• Strong grounding in statistics and data analysis, including study design considerations and interpretation of real-world clinical data
• Strong understanding of cancer biology, immunology, or human disease mechanisms
• Demonstrated experience analyzing large-scale biological datasets (e.g., NGS, RNA-seq, other genomics or transcriptomics data), ideally in oncology, immunology, or human disease
• Excellent written and verbal communication skills with comfort in client-facing roles
• Ability to thrive in a fast-paced, dynamic environment
• Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work
• Expertise in one or more of the following: Real-world evidence (RWE), survival analysis, causal inference, network/systems biology, or multimodal integration
• A strong history of peer-reviewed publications or conference presentations
• Understanding of the drug development lifecycle, from target discovery to clinical development
• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or master’s degree with 3+ years of relevant experience).

Required skills

• Agentic Frameworks, Cancer Biology, Client-Facing Communication, Clinical Data Analysis, Genomics and Transcriptomics Data Analysis, Large Language Models (LLMs), Machine Learning, Python, R, SQL, Statistics, Version Control

Preferred skills

• Written and Verbal Communication

Experience

• 3+ years