Scientist II, Computational Biology, Pharma R&D

The successful candidate will combine strong computational and statistical skills with a deep interest in biology and translational science. They will be comfortable working with real-world data, engaging with external scientific stakeholders, and leveraging AI (foundation models, Large Language Models, agentic systems, etc.) to scale tasks and augment insights.

Key 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.

Qualifications

• Education: PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or master’s degree with 3+ years of relevant experience).
• Technical Proficiency: 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.
• Scientific Knowledge: Strong understanding of cancer biology, immunology, or human disease mechanisms.
• Data Expertise: Demonstrated experience analyzing large-scale biological datasets (e.g., NGS, RNA-seq, other genomics or transcriptomics data), ideally in oncology, immunology, or human disease.
• Soft Skills: Excellent written and verbal communication skills with comfort in client-facing roles. Ability to thrive in a fast-paced, dynamic environment.

Preferred Skillsets

• Generative AI: Practical experience configuring or adapting LLMs, or using related tools/frameworks, to support scientific work.
• Specialized Modeling: Expertise in one or more of the following: Real-world evidence (RWE), survival analysis, causal inference, network/systems biology, or multimodal integration.
• Publication Record: A strong history of peer-reviewed publications or conference presentations.
• Drug Discovery Context: Understanding of the drug development lifecycle, from target discovery to clinical development.