Senior Scientist, Computational Biology, Life Science Strategy

Tempus AI•Chicago, IL

About The Position

Tempus is seeking an experienced Senior Scientist to join its Computational Biology, Life Science Strategy team, which works at the intersection of biological data science and AI and supports collaborations with pharmaceutical partners. The role integrates large-scale molecular and clinical datasets to generate actionable insights for drug discovery and development and develops research tools to improve the impact and efficiency of the team’s work. The Senior Scientist partners with internal and external stakeholders to deliver insights reflecting the biopharmaceutical landscape, including biomarkers and clinical development. The position performs reproducible computational analyses, evaluates and implements methods for real-world, clinical, and omics datasets, and incorporates AI and LLM tools into research workflows. It also contributes reusable code and scalable solutions, communicates findings to technical and non-technical audiences, and provides project leadership and mentorship.

Requirements

PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field, or a master’s degree with at least 3 years of relevant experience.
At least 2 additional years of relevant industry or postdoctoral experience.
Proven track record reflected in peer-reviewed publications.
Proficiency in R and/or Python, including common computational biology and scientific computing libraries.
Proficiency applying machine learning, LLM-based coding assistants, and agentic frameworks to biological or clinical research.
Good software engineering practices, including version control, modular code, and documentation.
Experience working with SQL and large relational databases.
Strong grounding in statistics and data analysis, including study design and interpretation of real-world clinical data.
Ability to provide quality code review and QA/QC for others’ work.
Strong understanding of cancer biology, immunology, or human disease mechanisms, and ability to interpret and summarize scientific findings.
Demonstrated experience analyzing large-scale biological datasets, such as NGS, RNA-seq, genomics, or transcriptomics data.
Demonstrated project leadership and people mentorship.
Excellent written and verbal communication skills and comfort in client-facing roles.
Strong collaboration skills and ability to thrive in a fast-paced, dynamic setting.

Nice To Haves

Practical experience configuring or adapting LLMs, or using related tools and frameworks, to support scientific work.
Expertise in one or more of real-world evidence, survival analysis, causal inference, network or systems biology, or multimodal integration.
Strong history of peer-reviewed publications or conference presentations.
Understanding of the drug development lifecycle, from target discovery through clinical development.

Responsibilities

Partner with life science strategy, market intelligence, pharmaceutical R&D, and other stakeholders to deliver high-impact data insights relevant to the biopharmaceutical landscape, biomarkers, and clinical development.
Translate early-stage concepts into well-developed analysis plans.
Perform robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data.
Apply statistical and computational best practices to generate insights on clinical trial design, patient selection, treatment response, resistance mechanisms, and disease biology.
Use LLMs and other AI tools to automate and accelerate coding, discovery, documentation, and review.
Design and prototype agentic workflows using foundation models and LLMs to generate insights and predictive models.
Evaluate, adapt, and implement methods for real-world, clinical, and omics datasets, including methods such as survival analysis, causal inference, and multimodal integration.
Contribute reusable code, internal packages, and best practices for use across collaborations and programs.
Collaborate with Research, Clinical, Data Science, and Engineering colleagues to refine analyses, build scalable solutions, and align scientific work with platform and product roadmaps.
Communicate methods and results to technical and non-technical stakeholders; prepare and present reports, external deliverables, and, where appropriate, manuscripts or conference materials.
Lead projects by setting priorities, coordinating resources, identifying obstacles, and ensuring milestones and deadlines are met.
Mentor junior scientists and promote high standards of collaborative project execution and delivery.