Senior Computational Biologist, Oncology Target Discovery

Recursion; New York (NY); Hybrid; USD 151,800 - 220,660; Full time

Benefits offered by this job

Annual bonus

Equity compensation

Comprehensive benefits package

Job summary

Recursion is seeking a Senior Computational Biologist in Early Discovery Oncology to join a collaborative, data‑driven team in New York or Salt Lake City. You will integrate large-scale omics data to nominate novel targets, validate disease biology, and shape precision oncology strategies that feed into drug discovery.

The role emphasizes cross‑functional collaboration with biologists, chemists, and engineers, applying probabilistic and ML methods in Python or R to analyze diverse clinical

Qualifications

• Strong stakeholder management and technical communication skills.
• Experience applying computational methods to integrate complex biological data.
• Proficiency in Python or R for data analysis and visualization.

Responsibilities

• Drive target discovery by evaluating molecular evidence for nomination and validation.
• Pilot novel methods for target discovery, validation, and early patient population hypotheses.
• Define internal criteria for target data packages and early population strategy.
• Collaborate with platform and engineering teams to accelerate programs.
• Present data analyses to decision makers to advance programs toward IND.

Education

PhD in computa­tional biology / bioinformatics / systems biology / cancer biology

MS in a relevant field with 5+ years biotech/pharma experience

Tools

Python

R

Job description

Recursion is seeking a Senior Computational Biologist in Early Discovery Oncology to join a collaborative, data‑driven team in New York or Salt Lake City. You will integrate large-scale omics data to nominate novel targets, validate disease biology, and shape precision oncology strategies that feed into drug discovery.

The role emphasizes cross‑functional collaboration with biologists, chemists, and engineers, applying probabilistic and ML methods in Python or R to analyze diverse clinical