Tempus
Senior Scientist II, Computational Biology, Pharma R&D
$100K–$175K
Boston +2 · Senior · Full-time · Posted Oct 20, 2026
- Location
- Boston
Chicago
New York City - Experience
- An additional 2+ years of relevant industry or post-doctoral experience; alternatively, a master’s degree with 4+ years of relevant experience.
- Education
- PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field (or Masters degree with 4+ years of relevant experience).
About the job
Leads computational biology analyses and pharmaceutical collaborations, integrating molecular and clinical data to support drug discovery, development, and research tools.
What you'll do
- Translate pharmaceutical partners’ scientific needs into analytical plans and deliver insights for target discovery, biomarkers, clinical trials, and treatment response.
- Analyze multimodal biological and clinical datasets, develop methods, and contribute reusable code and scalable research tools.
- Prototype AI and LLM workflows, lead projects, mentor junior scientists, and communicate findings to technical and non-technical stakeholders.
What they ask for
- PhD in a related field, or a master’s degree with 4+ years of relevant experience.
- Proficiency in R and/or Python, machine learning, LLM-based coding assistants, and agentic frameworks for biological or clinical research.
- Strong statistics and data analysis skills, and experience analyzing large-scale biological datasets.
- Demonstrated project leadership and mentorship; peer-reviewed publication track record.
Nice to have
- Practical experience configuring or adapting LLMs for scientific work; expertise in RWE, survival analysis, causal inference, network/systems biology, or multimodal integration.
- Experience with SQL and large relational databases; understanding of the drug development lifecycle.
Pay
$100,000-$160,000 USD base salary; $115,000-$175,000 USD base salary
Tools
R, Python, Copilot, Cursor, SQL, LLMs
AI focus
Incorporate LLMs and AI tools into research workflows; design agentic workflows using foundation models and LLMs for insights and predictive models.
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