Job Description

Computational BiologistOdyssey Therapeutics is propelling drug development beyond what is now possible to deliver medicines that address critical needs of patients with inflammatory and immunology diseases. We achieve unprecedented speed and efficiency by bringing together a target-centric approach, a toolbox of cutting-edge technologies, and a team of accomplished, world-class drug hunters. By reimagining the drug development process, we are creating a deep and broad drug pipeline that holds the potential to transform human health.The opportunity:Odyssey Therapeutics is seeking a Computational Biologist to support target discovery, translational biology, biomarker development, and clinical data analysis across our portfolio of autoimmune and inflammatory disease programs.This role will work closely with Discovery Biology, Translational Medicine, Clinical Development, and external collaborators to analyze and integrate genomic, transcriptomic, and clinical datasets to generate actionable biological insights that support therapeutic hypothesis generation, patient stratification, and clinical development strategies.Your primary objectives will be:Analyze internal and publicly available omics datasets to support target discovery, translational research, and therapeutic hypothesis generation.Develop, maintain, and optimize robust bioinformatics pipelines for the analysis of multi-omics datasets.Analyze GWAS, eQTL, transcriptomic, proteomic, and other human disease datasets to identify disease-relevant pathways, therapeutic targets, and biomarkers.Evaluate clinical and translational datasets to identify biomarkers associated with therapeutic response, resistance, and disease progression.Collaborate closely with Discovery Biology, Chemistry, Translational Medicine, and Clinical Development teams to integrate diverse datasets and generate actionable biological insights.Present scientific findings to cross-functional teams and contribute to internal reports, scientific presentations, publications, and external communications.About you:Ph.D. (or equivalent experience) in Computational Biology, Bioinformatics, Genetics, Immunology, Systems Biology, or a related discipline.2+ years of experience developing bioinformatics pipelines and applying computational approaches to immunology and/or drug discovery in an industry or academic setting.Deep expertise in computational biology, bioinformatics, and statistical analysis of large-scale biological datasets.Experience analyzing GWAS, whole-genome sequencing (WGS), and other human genetics datasets.Experience working with large public genomics resources such as GEO, ArrayExpress, UK Biobank, All of Us, Open Targets, FinnGen, or similar repositories.Demonstrated expertise in developing pipelines for bulk and single-cell omics technologies, including RNA-seq, scRNA-seq, CITE-seq, ATAC-seq, ChIP-seq, WGS, and WES.Strong proficiency in R and/or Python, with experience using version control and developing reproducible, standardized analysis workflows.Ability to rapidly learn and apply new computational methods and emerging omics technologies.Broad understanding of molecular biology, cell biology, biochemistry, and translational research.Familiarity with autoimmune, inflammatory, or immune-mediated diseases.Experience managing and analyzing large, complex datasets across multiple research programs.Proven ability to work collaboratively with computational scientists, biologists, chemists, and cross-functional project teams.Strong scientific communication skills, with the ability to independently perform analyses and clearly communicate findings to both computational and non-computational audiences.Demonstrated scientific contributions to computational biology and drug discovery through publications, presentations, or other impactful research.Creative, scientifically rigorous, and motivated to solve challenging biological problems in a collaborative environment.Experience supporting translational medicine, biomarker discovery, or clinical development programs is preferred.Experience integrating multi-modal omics datasets and applying AI or machine learning approaches to biological data analysis is preferred.

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