Genentech seeks a talented and highly motivated computational Scientist to engage in therapeutic and biomarker discovery efforts within our reverse translational research organization in Oncology Biomarker development in collaboration with Discovery Oncology departments. The successful applicant will work with interdisciplinary teams, to engage in research efforts focused on translation of complex molecular biomarker data, derived from clinical trial samples, into actionable biological insights that can guide our understanding of drug activity in patient tumors, characterize mechanisms of primary and acquired drug resistance, and identify subsets of patients who may benefit from specific therapeutic interventions. The ability to collaborate with a diverse group of scientists, present complex results in a clear and concise manner that is accessible to a diverse audience of quantitative, experimental, and clinical scientists, and publish scientific/methodological papers on a regular basis are important components of this position.
• Postdoctoral training or exceptional PhD experience in bioinformatics, biostatistics, computational biology, or a similar field. Alternatively, PhD in molecular biology, immunology, etc. combined with a very strong record of high-throughput data analysis, supported by publication in this area.
• Excellent communication and data presentation skills, including the ability to present complex scientific results to a diverse audience.
• Ability to work successfully as the bioinformatics lead in an interdisciplinary team
• Intense curiosity about the biology of disease and an eagerness to contribute to scientific efforts focused on understanding the molecular mechanisms underlying disease etiology and resistance to drug therapy.
• Relevant biological knowledge in oncology, oncogenic signaling pathways, and/or oncogenic dependencies.
• Deep understanding of computational techniques for analyzing high-throughput genomic and transcriptomic datasets and an interest in applying novel approaches to explore and integrate such data (whole exome sequencing, bulk and single-cell RNA-seq, cell-free DNA sequencing, etc).
• Demonstrated competence in a programming language such as R or python, experience with version control systems, and familiarity with high-performance computing environments.