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Salary: USD 90,000 - 150,000 / annual
Tempus is seeking a Scientist II in Computational Biology to join the Pharma R&D team. This role sits at the intersection of biological data science and AI, supporting collaborations with major pharmaceutical partners on drug discovery and development initiatives.
You will integrate large-scale molecular and clinical datasets to generate actionable insights for pharma collaborators, focusing on target discovery, biomarker development, and clinical development. Key responsibilities include:
• Executing computational research plans leveraging Tempus's multimodal platform to address key questions in drug development and patient selection
• Performing robust, reproducible analyses integrating genomic, transcriptomic, imaging, and clinical data using appropriate statistical and computational methods
• Incorporating LLMs, agentic workflows, and foundation models into daily workflows to accelerate code development, discovery, and insight generation
• Evaluating and implementing new methods for analyzing real-world, clinical, and omics datasets (survival analysis, causal inference, multimodal integration)
• Contributing reusable code, internal packages, and best practices applicable across multiple collaborations
• Collaborating cross-functionally with Research, Clinical, Data Science, and Engineering teams
• Communicating complex methods and results to technical and non-technical stakeholders; preparing internal reports, external deliverables, and scientific manuscripts
The ideal candidate combines strong computational and statistical skills with deep interest in biology and translational science. You will be comfortable working with real-world data, engaging with external scientific stakeholders, and leveraging AI to scale tasks and augment insights.
REQUIREMENTS:
• Education: PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or related field (or Master's degree with 3+ years relevant experience)
• Technical Proficiency: Proficiency in R and/or Python with experience in computational biology and scientific computing libraries; experience with SQL and large relational databases
• Machine Learning & AI: Proficiency using machine learning, LLM-based coding assistants (Claude Code, Codex), and agentic frameworks for biological/clinical research
• Software Engineering: Adherence to good practices including version control, modular code, and documentation
• Statistics: Strong grounding in statistics and data analysis, including study design and interpretation of real-world clinical data
• Scientific Knowledge: Strong understanding of cancer biology, immunology, or human disease mechanisms
• Data Expertise: Demonstrated experience analyzing large-scale biological datasets (NGS, RNA-seq, genomics, transcriptomics), ideally in oncology, immunology, or human disease
• Communication: Excellent written and verbal communication skills with comfort in client-facing roles; ability to thrive in fast-paced environments
PREFERRED:
• Practical experience configuring or adapting LLMs for scientific work
• Expertise in real-world evidence (RWE), survival analysis, causal inference, network/systems biology, or multimodal integration
• Strong publication record in peer-reviewed journals or conference presentations
• Understanding of drug development lifecycle from target discovery through clinical development
