Principal Scientist, Translational Computational Biology

• 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

• 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, Pheno Cycler)
• familiarity with oncology drug development and biomarker strategies
• proven ability to translate complex data into actionable insights

• 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

Oncology, spatial biology, multimodal patient data, early-phase clinical support, biomarker strategy, and computational innovation in drug discovery.

Likely hybrid or onsite in Cambridge, with collaboration across multi-disciplinary teams.