Genentech seeks a talented and highly motivated computational biology / Bioinformatics scientist to pursue reverse translational data-driven projects in close collaboration with our Cancer Immunology and Oncology Biomarker Development Departments.
Recently, exciting developments at Genentech and elsewhere have demonstrated the potential of leveraging the human immune system for effective and durable control of cancer. Our extensive bulk and single-cell RNA-seq characterization of samples from cancer immunotherapy clinical trials provide an outstanding opportunity for rapidly taking clinical insights back into the laboratory.
The primary focus of this position is the identification of signals in single-cell and bulk RNA-seq data that are associated with the variability in human immunotherapy treatment response. This will form the basis to further investigate the mechanistic basis for the clinical observations.
Applicants should be able to work comfortably on an interdisciplinary team, have a strong desire to carry out data analysis and integration across various domains (High-throughput Transcriptomic, Proteomic, Epigenomic data), and apply best-in-class algorithms — or develop new algorithms — that directly address the motivating biological and clinical questions. Regular publication of scientific results is strongly encouraged. Finally, applicants should be able to effectively present complex results in a clear and concise manner that is accessible to a diverse audience of quantitative, experimental, and clinical scientists.
Who you are
- You have a PhD in Computational Biology, Biostatistics, Bioinformatics or similar, with a strong publication record. Alternately, you have a PhD in Immunology, Molecular Biology, etc. combined with a very strong record of high-throughput data analysis, supported by publication in this area.
- You have a solid understanding of the relevant concepts in cancer biology and genetics, or in immunology. You also have enthusiasm for learning more.
- You have postdoctoral experience in basic or translational research either in an academic or industry setting with a record of publication in lead positions.
- You have a broad experience with data generated by one or more high-throughput molecular assays: Next-generation sequencing, flow cytometry, etc. If you have additional experience with single-cell assays (e.g., single-cell RNA-seq or CyTOF) it is a significant plus.
- You understand the statistical principles behind current best practices in high-throughput molecular data analysis.
- You have a strong experience in the use of a high-level programming language such as R or Python for complex data analysis.
- You have exceptionally strong communication, data presentation and visualization skills.
- You enjoy working both independently and collaboratively, and feel comfortable to handle several concurrent, fast-paced projects.
What to expect from us
- A highly collaborative and dynamic research environment where we aim to translate our understanding of cancer biology and immunology to develop personalized therapies and diagnostics to transform clinical practice to benefit patient health.
- Access to clinical data sets and samples.
- Access to state-of-the-art technologies and pioneering research.
- Participation in seminar series featuring academic and industry scientists.
- Campus-like lifestyle with a healthy work-life balance.
- Mentored opportunities to further develop professional skills.