Senior Scientist, Computational Biology, Life Science Strategy

Tempus; Chicago (IL); On-site; USD 150,000 - 190,000; Full time

Job summary

Tempus seeks a Senior Scientist in Computational Biology to advance data-driven insights across biomarker development and clinical data integration. You will lead analyses spanning genomic, transcriptomic, imaging, and clinical data, applying ML techniques and state-of-the-art AI tools to drive real-world decision-making and drug development strategies.

You will collaborate across Life Science Strategy, Research, and Engineering, deliver reproducible analyses, and mentor junior scientists while

Qualifications

• PhD in Computational Biology, Bioinformatics, Biostatistics, ML or related field, or a Masters with 3+ years in industry/research.
• Strong programming in R and/or Python with scientific computing libraries.
• Experience applying ML/AI methods to biological/clinical data.
• Proficiency with SQL and large relational databases.
• Excellent communication of complex methods to technical and non-technical stakeholders.

Responsibilities

• Lead data-driven analyses integrating multi-omic and clinical data to inform drug discovery and development.
• Develop robust, reproducible computational analyses and best practices.
• Incorporate AI/LLMs into workflows to accelerate code development and research output.
• Contribute reusable code and pipelines across collaborations.
• Collaborate with R&D, Clinical, Data Science, and Engineering teams.
• Present findings in internal reports and external materials.
• Provide project leadership and mentorship to junior scientists.

Skills

R

Python

Machine Learning

LLM tools

SQL

Statistics

Data analysis

Code QA/QC

Education

PhD in Computational Biology

Masters with 3+ years of relevant experience

Tools

Copilot

Cursor

Job description

About Tempus

Tempus is advancing precision medicine through the power of artificial intelligence and the world's largest library of clinical and molecular data. Our mission is to enable real-time, data-driven decision-making to improve patient outcomes and accelerate drug development.

About the Role

We are seeking an experienced Senior Scientist to join the Computational Biology, Life Science Strategy team. This group operates at the intersection of biological data science and AI, supporting high-impact collaborations with major pharmaceutical partners and our life science strategy. The ideal candidate will demonstrate strong scientific leadership, deep expertise in computational biology including AI tools, and a proven track record of success. The role focuses on integrating large-scale molecular and clinical datasets, generating actionable insights for drug discovery and development, and building next-generation research tools that enhance the impact and efficiency of our work.

Key Responsibilities

• Life Science Strategy: Partner with internal and external stakeholders including life science strategy, market intelligence and pharma R&D to deliver high impact data insights that reflect the current biopharma landscape including around biomarker and clinical development. Independently translate rough concepts into well thought out analysis plans.
• Computational Analysis & Insight Generation: Execute robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data. Apply appropriate statistical and computational best practices to derive insights related to clinical trial design, patient selection, treatment response, resistance mechanisms, and disease biology.
• AI & LLM Innovation: Incorporate LLMs and other AI tools into day-to-day workflows to automate, streamline and accelerate code development, discovery, documentation, and review. Design and prototype agentic workflows integrating foundation models and LLMs to power new insights and predictive models.
• Method Development and Platform Contribution: Evaluate, adapt, and implement new methods for the analysis of real-world, clinical, and 'omic datasets (e.g., survival analysis, causal inference, multi-modal 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, build scalable solutions, and align scientific efforts with platform and product roadmaps.
• 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.
• Leadership: demonstrate strong project-level leadership, to ensure the delivery of high-quality results in an efficient and effective manner. This includes setting clear priorities, coordinating resources, and proactively identifying and addressing potential obstacles to ensure that project milestones and deadlines are consistently met. The ideal candidate will foster a collaborative environment, provide mentorship to junior scientists on project teams, and uphold a standard of excellence in project execution and delivery.

Qualifications

• Education: PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or Masters degree with 3+ years of relevant experience).
• Plus an additional 2+ years of relevant industry or post-doctoral experience.
• Track record of success: proven in peer reviewed publications.
• Technical Proficiency: Proficiency in R and/or Python, including experience with common computational biology and scientific computing libraries.
• Proficiency applying machine learning, LLM-based coding assistants (e.g., Copilot, Cursor) 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.
• Proficiency in providing quality code review and QA/QC for the work of others.
• Scientific Knowledge: Strong understanding of c