Principal Scientist, Computational Biology-Oncology Translational Research

Role: Principal Scientist in Computational Biology for Oncology Translational Research, focusing on developing and executing computational strategies for biomarker programs in cancer drug development. Responsibilities: lead analysis of multimodal omics data (DNA/RNA sequencing, proteomics, single-cell, spatial transcriptomics), define and defend statistical and machine-learning approaches, integrate molecular, pathological, and clinical data to assess biomarker relevance and utility, and support biomarker strategies such as patient selection, MRD monitoring, and clinical interpretation. Collaborate across multidisciplinary teams, defend analytical recommendations, and contribute to scientific publications. Requirements: PhD in Bioinformatics, Computational Biology, Biostatistics, or related field; 3+ years industry experience; advanced proficiency in R, Python, Linux/Unix; deep oncology expertise, including knowledge of cancer biology, biomarker applications, and clinical trial phases (Ph1-3); expertise in statistical and machine-learning methods for high-dimensional data; understanding of IND/NME development and oncology trial design. Preferred: familiarity with CDISC data models, deep learning, foundation models, and biomarker activities supporting diagnostics and regulatory submissions. High-Value: oncology, biomarker development, multimodal omics, AI-enabled bioinformatics, early to late clinical phases. Location: Cambridge, MA; La Jolla, CA; Spring House, PA; hybrid work arrangement. No explicit travel details specified.