Senior Computational Biologist - Translational and Clinical Biomarkers

Role Summary

Senior Computational Biologist focused on translational and clinical biomarkers. You will lead computational efforts for IND and clinical-stage drug development programs, evaluating therapeutic hypotheses and identifying candidate biomarkers to measure in translational studies and early clinical trials. You will collaborate with biologists, clinicians, platform and data engineers, and translational experts to scale methods that bring patient insights and reverse translation to the portfolio.

Responsibilities

• Evaluate the molecular evidence for predictive and PD biomarker hypotheses in translational models and clinical samples (DNA, RNA, ctDNA, and novel exploratory modalities)
• Pilot novel methods for patient stratification and indication selection or expansion
• Deliver biological insights on therapeutic candidates and disease biology from the analysis of high dimensional (phenomics, transcriptomics, patient-derived) datasets
• Present data analysis to decision makers and stakeholders in a clear and compelling way that drives toward getting medicines to patients
• Industrialize analysis approaches to not only solve for the current project, but also accelerate future projects and scale the impact that we can have
• Collaborate cross-functionally with Recursion’s data science, platform, ML and clinical teams to further advance Recursion’s ability to leverage our own clinical data in meta-analysis, hypothesis generation, and reverse translation

Qualifications

• Required: PhD in a relevant field (computational biology, systems biology, bioinformatics, cancer biology, immuno-oncology, etc.) with a very strong computational focus and 3+ years of biotech or pharma industry experience OR MS in a relevant field and 5+ years of biotech or pharma industry solving fundamental problems in oncology or drug discovery
• Required: Experience with high dimensional patient biomarker data from clinical trials in oncology
• Required: Strong understanding of patient genetics and druggability of disease relevant pathways
• Required: Experience applying computational methods (including probabilistic, statistical, and/or machine learning techniques) to analyze and integrate complex biological and/or human clinical data in Python or R
• Required: Deep expertise in the analysis and data integration of two or more ‘omics data modalities (phenomics, transcriptomics, proteomics, genomics) including experience with matched clinical and molecular patient data
• Required: Exceptional data visualization skills
• Required: Excellent cross-functional communication skills, including an ability to explain complex scientific concepts to a variety of audiences using plots, documents, and presentations
• Preferred: Experience with late stage drug discovery and IND submission
• Preferred: Experience collaborating cross-functionally with biometrics, statistical sciences and clinical pharmacology departments