Staff Computational Biologist

Role Summary

Staff Computational Biologist at Recursion. Lead computational efforts across early-stage drug discovery programs, define research strategy using platform data, and collaborate with biology, chemistry, and engineering teams to translate large-scale data into therapeutic programs.

Responsibilities

• Evaluate molecular evidence for therapeutic hypotheses and accelerate drug program progression into the clinic
• Architect quantitative analyses and experiment cascades that combine diverse datasets to drive key program decisions
• Pilot novel methods for patient stratification and indication selection or expansion
• Deliver biological insights on therapeutic candidates and disease biology from high-dimensional datasets (phenomics, transcriptomics, patient-derived) to support proof-of-concept model selection and asset prioritization
• Present data analysis to decision makers and stakeholders in a clear and compelling way to drive toward getting medicines to patients
• Industrialize analysis approaches to solve current projects and accelerate future projects, scaling impact
• Collaborate cross-functionally with data science, platform, ML, and clinical teams to translate large-scale data assets into therapeutic programs

Qualifications

• PhD in computational biology, systems biology, bioinformatics, cancer biology, immuno-oncology, or related field with strong computational focus and 5+ years in biotech/pharma, or MS with 7+ years in biotech/pharma solving fundamental problems in oncology/drug discovery
• Experience with two or more disease indications; strong understanding of patient genetics and druggability of relevant pathways
• Experience in early target discovery, hit identification and hit-to-lead stage gates
• Experience applying computational methods (probabilistic, statistical, machine learning) to analyze and integrate complex biological/human clinical data in Python or R
• Deep expertise in two or more omics modalities (phenomics, transcriptomics, proteomics, genomics) including matched clinical and molecular patient data
• Exceptional data visualization skills
• Excellent cross-functional communication, able to explain complex scientific concepts to diverse audiences using plots, documents, and presentations

Nice To Have

• Experience analyzing imaging-based data
• Experience prioritizing compounds during SAR and lead optimization cycles

Education

• PhD or MS in a relevant field with strong computational focus as described above