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 include leading analyses of multimodal omics data (DNA/RNA sequencing, proteomics, single-cell, spatial transcriptomics), integrating molecular and clinical data to assess biomarker relevance and utility, and supporting AI-enabled bioinformatics and machine learning approaches. The role involves collaborating with cross-functional teams across Translational Research, Clinical Development, Data Science, and Regulatory functions, to inform clinical trial design, patient stratification, and biomarker validation. Qualifications: PhD in Bioinformatics, Computational Biology, Biostatistics, or a related field, with at least 3 years in Pharma or biotech. Proficiency in R, Python, Linux/Unix, and experience with high-throughput sequencing and multimodal omics data analysis is required. Deep oncology expertise, particularly in clinical trial phases 1-3, biomarker applications, and cancer biology, is essential. Advanced knowledge of statistical/machine learning methods and experience with IND/clinical development processes are necessary. Preferred skills include familiarity with CDISC data standards, deep learning, foundation models, and biomarker activities supporting diagnostics and regulatory submissions. High-value specifics: Oncology (cancer), multimodal biomarker data, translational research, late-stage clinical trials, AI and bioinformatics tools. Reporting lines and work setup are not specified.