• Access, analyze and integrate publicly available oncogenomics information to enable preclinical experimental design for research teams.
• Bring in expertise in statistics, mathematics and computer science to enhance our computational biology capabilities; lead the discussion on rigorous statistic methods and novel computational and system biology algorithms in the computational biology group.
• Work with external resources to generate and analyze tumor genome / transcriptome profiling data for pre-clinical models; integrate with internal and external large scale non-clinical and clinical data sets to understand mechanism of compound response, aimed to inform additional patient selection / enrichment marker development, additional indications, and rational combination strategies.
• Enable new asset diligence from cancer informatics perspectives: integrate cancer genomic data to help evaluate new targets and agents.
• Ph.D. or equivalent computational/statistical experience and training with 5+ years of applied experience in Pharma/Biotech; oncology and oncogenomics experience preferred.
• Experience in large scale, multi-dimensional genomic (including but not limited to NGS) data analysis, integration and management, with proficiency in data visualization.
• Proven ability to collaborate and influence across multi-disciplinary teams of internal colleagues and interact with external experts in academic institutions as well as CROs.
• A high level of integrity and desire to find new medicines that bring benefit to patients; a strong intellectual curiosity and desire to understand and solve problems; a willingness to work pro-actively in a fast-paced and dynamic work environment.
• Flexibility to work with internal and external systems, especially cloud based computing and storage infrastructure; AWS experience desired.
• Creative and practical use of systems biology methods in gene/target discovery, interpretation of cellular processes, and biomarker discovery.