Senior Scientist II, Computational Biology, Pharma R&D

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 4+ 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). 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 cancer biology, immunology, or human disease mechanisms, with the ability to interpret and effectively summarize scientific findings.
• 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.
• Scientific Leadership: Demonstrated project leadership and people mentorship.
• Soft Skills: Excellent written and verbal communication skills with comfort in client-facing roles. Strong collaborator. Ability to thrive in a fast-paced, dynamic setting.

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 multi-modal 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.
• Experience working with SQL and large relational databases.