Principal Scientist, Translational Computational Biology

Role: Principal Scientist, Translational Computational Biology within BMS's Oncology Translational IPS team, focusing on embedding computational expertise into oncology drug development from discovery through early clinical phases. Responsibilities: analyze multimodal patient-derived datasets—including spatial transcriptomics, spatial proteomics, RNA-seq, WES, ctDNA, TCR-seq, and flow cytometry; support biomarker and patient stratification strategies; interpret data to inform target validation, IND-enabling studies, and early clinical decisions; develop and deploy AI-enabled methods for evidence integration and hypothesis generation; lead computational ownership of spatial biology platforms; mentor junior scientists and enhance portfolio capabilities through scalable, reproducible workflows. Requirements: Ph.D. in computational biology, bioinformatics, or related, with 4+ years relevant experience, or equivalent with additional years; strong programming skills in R/Python; experience with spatial biology platforms (Xenium, Visium, COMET, PhenoCycler); familiarity with oncology drug development and biomarker strategies; proven ability to translate complex data into actionable insights. Preferred: expertise in spatial transcriptomics and proteomics, AI and machine learning applications in translational science, perturbation biology, and regulatory network analysis; experience in oncology translational research or early clinical development; publication record in methods development. High-Value: Oncology, spatial biology, multimodal patient data, early-phase clinical support, biomarker strategy, and computational innovation in drug discovery. Work Setup: Likely hybrid or onsite in Cambridge, with collaboration across multi-disciplinary teams.