Backed by NEA.
About the role
• Pharma Collaboration & Strategy: Partner with pharmaceutical collaborators to execute computational research plans that leverage the Tempus multimodal platform to address key questions in target discovery, biomarker development, and clinical development. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Education: PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or master’s degree with 3+ years of relevant experience)
- Technical Proficiency: Proficiency in R and/or Python, including experience with common computational biology and scientific computing libraries
- Proficiency in using machine learning, LLM-based coding assistants (e.g., Claude Code, Codex), 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
More about this role
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.
We are seeking a Scientist II to join our Computational Biology, Pharma R&D team. We work at the intersection of biological data science and AI to support collaborations with major pharmaceutical partners. 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.
The successful candidate will combine strong computational and statistical skills with a deep interest in biology and translational science. They will be comfortable working with real-world data, engaging with external scientific stakeholders, and leveraging AI (foundation models, Large Language Models, agentic...
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