Senior Scientist, Computational Biology, Pharma R&D

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.

About the Role:

We are seeking an experienced Senior Scientist to join the Computational Biology, Pharma R&D team. This group operates at the intersection of biological data science and AI, supporting high-impact collaborations with major pharmaceutical partners. 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

• Pharma Collaboration & Strategy: Partner with pharmaceutical collaborators in target discovery, biomarker development, and clinical development teams.  Understand their scientific and clinical objectives, pipelines, and drug modalities. Independently translate partner needs into key questions and technical requirements to design well-scoped analytical plans that leverage the Tempus multimodal platform.

• 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