The Position Genentech seeks a talented and highly motivated candidate for a computational biology position in discovery oncology research. We are looking for a computational scientist who is excited about collaborating with world-class scientists with diverse backgrounds to move science and drug development forward with the goal of making a true difference for patients. The Discovery Oncology group is focused on developing therapies targeting major pathways involved in cancer. Our research efforts are rooted in a deep understanding of cancer genomics spanning patient data (i.e. TCGA and other data sources), internal clinical data to pre-clinical models of cancer (i.e. PDX, GEMMs and tumor cell lines). Through experimental and computational approaches, our goal is to understand the mechanistic basis of therapeutic response and resistance as well as to discover novel targets/pathways in tumors. Additionally, we are actively looking for biomarkers that can help match our drugs to the patients that would benefit most from treatment. In our research we apply a wide variety of high-throughput data technologies and utilize many big genomic data sets from both internal and public sources to guide our studies. Therefore, we are looking for a candidate who is exceptionally competent with computational tools and their application to a diverse set of large-scale data but also has the biological knowledge to be able to ask the right questions and place the results in the appropriate context to effectively move research forward. A successful candidate will collaborate closely with an interdisciplinary team of computational and molecular scientists and will help design experiments, carry out analysis of a wide variety of data and integrate results with current biological knowledge. Regular publication of scientific and methodological results is highly encouraged. The candidate should have extensive experience with data generated by high-throughput molecular assays such as next- generation sequencing, single cell sequencing, epi-genomics technologies such as ChIP-Seq or ATAC-Seq, and/or high-throughput screening data such as pharmacological screening or CRISPR screening. Further, the successful candidate should be able to effectively present complex results in a clear and concise manner — to other computational scientists as well as to audiences with other scientific backgrounds. Requirements PhD in bioinformatics, biostatistics, computational biology or similar, with a strong publication record. Alternately, a PhD in molecular biology, oncology, etc. combined with a very strong record of high-throughput data analysis, supported by publication in this area. A very strong understanding of the relevant concepts in cancer biology and molecular biology. Broad experience with data generated by one or more high-throughput molecular assays as detailed above. An understanding of the statistical principles behind current best practices in high- throughput molecular data analysis. Strong experience in the use of a high-level programming language such as R (preferred), MATLAB, Python or Perl for complex data analysis. Exceptionally strong communication, data presentation and visualization skills. Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects.
