Senior Scientist, Computational Biology, Life Science Strategy

Tempus AI • Boston, MA

7d • $100,000 - $175,000

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 in support of collaborations with pharmaceutical partners. The role integrates large-scale molecular and clinical datasets to generate actionable insights for drug discovery and development and builds research tools to improve the impact and efficiency of the work.

The Senior Scientist partners with internal and external stakeholders, including life science strategy, market intelligence, and pharmaceutical R&D, to deliver insights reflecting the biopharma landscape, including biomarkers and clinical development. The role independently translates early concepts into analysis plans, conducts reproducible computational analyses, evaluates and implements methods, and contributes reusable code and best practices. It also applies AI tools and develops agentic workflows to support research and predictive modeling.

The role collaborates across Research, Clinical, Data Science, and Engineering; communicates methods and findings to technical and non-technical audiences; and prepares reports, partner deliverables, and, where appropriate, manuscripts or conference materials. It provides project-level leadership, coordinates priorities and resources, addresses obstacles, supports junior scientists through mentorship, and ensures high-quality delivery against project milestones.

Requirements

• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field; alternatively, a master's degree with at least 3 years of relevant experience.
• At least 2 additional years of relevant industry or postdoctoral experience.
• A proven track record of success demonstrated through 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 such as Copilot or Cursor, 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 considerations and interpretation of real-world clinical data.
• Ability to conduct 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; oncology, immunology, or human disease experience is relevant.
• Demonstrated project leadership and people mentorship.
• Excellent written and verbal communication skills, comfort in client-facing roles, and strong collaboration skills.
• 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, and multimodal integration.
• A strong history of peer-reviewed publications or conference presentations.
• Understanding of the drug development lifecycle, from target discovery through clinical development.

Responsibilities

• Partner with internal and external stakeholders, including life science strategy, market intelligence, and pharmaceutical R&D, to deliver insights about the biopharma landscape, biomarkers, and clinical development.
• Translate early-stage concepts into well-developed analysis plans.
• Conduct robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data.
• Apply statistical and computational best practices to generate insights related to clinical trial design, patient selection, treatment response, resistance mechanisms, and disease biology.
• Incorporate LLMs and other AI tools into workflows to accelerate code development, discovery, documentation, and review.
• Design and prototype agentic workflows using foundation models and LLMs to support new 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, develop scalable solutions, and align scientific work with platform and product roadmaps.
• Communicate complex methods and results to technical and non-technical stakeholders.
• Prepare and present internal reports, external-facing deliverables, and, where appropriate, manuscripts or conference materials.
• Lead projects by setting priorities, coordinating resources, identifying and addressing obstacles, and ensuring milestones and deadlines are met.
• Mentor junior scientists on project teams and uphold high standards for project execution and delivery.

Benefits

• Full range of benefits, which may include incentive compensation, restricted stock units, medical benefits, and other benefits depending on the position.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree