Senior Computational Biologist - Translational and Clinical Biomarkers

Senior Computational Biologist - Translational and Clinical Biomarkers

Your work will change lives. Including your own.

The Impact You’ll Make

As a key member of Recursion's portfolio-facing data science team, you will be at the forefront of reimagining drug discovery from first principles using Recursion’s massive data and compute capabilities. You will be the primary computational lead for multiple IND and clinical stage drug development programs and will be expected to use platform and patient data to advance our most promising therapeutic candidates through clinical trials.

You will be responsible for the evaluation of therapeutic hypotheses using internal and external data to identify candidate biomarkers to measure in translational studies and in phase I-II clinical trials. This is a highly collaborative role: you will partner with biologists, clinicians, platform and data engineers, and translational experts to develop and scale methods that bring patient insights and reverse translation to the forefront of our medicines portfolio.

The ideal candidate has strong stakeholder management, the ability to independently scope and prioritize with ambiguous or conflicting information, and is motivated to look under every stone to do the right thing for patients.

• 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

Our group is a bold, agile, diverse collective of computational drug discovery scientists deeply focused on the singular goal of bringing new therapeutics into the clinic at an accelerated pace. You will work extensively with scientists across the organization to advance milestones, provide insights, and drive decisions that advance Recursion’s capabilities and increase our likelihood of success. Essential attributes for this role include a bold, execution‑first attitude and passion for deploying rigorous science to develop life‑changing medicines.

We partner closely with biologists, translational scientists, and clinicians to design and execute decisional data analysis for multiple programs. We are responsible for data strategy across the portfolio and are supported by computational leadership in designing scalable and reproducible experiments that advance multiple programs  team works closely with computational biologists in other therapeutic areas (neurobiology, immunology and inflammatory diseases, etc.) as well as data scientists and engineers from our core platform teams to provide a strong network of feedback and support.

• 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 experience in biotech or pharma industry OR MS in a relevant field and 5+ years of experience in biotech or pharma industry solving fundamental problems in oncology or drug discovery
• Experience with high dimensional patient biomarker data from clinical trials in oncology
• Strong understanding of patient genetics and druggability of disease relevant pathways
• Experience applying computational methods (including probabilistic, statistical, and/or machine learning techniques) to analyze and integrate complex biological and/or human clinical data in a high level…