Principal Scientist, Translational Computational Biology
Core
Principal scientist translating patient-derived molecular, spatial, and clinical data into biomarker hypotheses, patient stratification strategies, and decision-grade recommendations for oncology drug development.
Role type
Principal Scientist, Translational Computational Biology
Builds
Biomarker strategies, patient selection plans, and reusable AI-enabled analytical workflows for oncology programs
Domain
Biopharma / Oncology / Computational Biology
Deliverable
production ML models
Required skills
Drug development lifecycle knowledge, spatial transcriptomics analysis, spatial proteomics analysis, multimodal data integration, AI/LLM method design, causal inference, regulatory network analysis, scientific mentorship
Preferred skills
Digital pathology experience, cloud compute expertise, agentic workflow design
Technologies
Xenium, Visium, Visium HD, COMET, PhenoCycler, RNA-seq, ctDNA, WES, TCR-seq, CTC, flow cytometry, IHC, proteomics, LLMs
Responsibilities
Shape biomarker strategy and patient stratification hypotheses; analyze and integrate multimodal molecular and clinical datasets; design and deploy AI approaches for evidence integration; build reproducible cloud-ready pipelines for spatial and single-cell data; mentor junior scientists and document methods to publication standards
Seniority
Principal, hands-on IC with strategic influence
