Computational Biologist at Genentech

Work on cutting-edge research projects in Oncology Bioinformatics at Genentech. We develop/apply novel computational concepts to molecular data to understand cancer biology and develop new drugs.

The candidate for this role will have a strong background in computational biology and -omics data analysis, an excitement to contribute to early-stage drug programs, and at least some experience leveraging advanced computational models or machine learning / deep learning frameworks. In this role, you will contribute to basic research in Oncology Bioinformatics, applying these approaches to improve our understanding of molecular dependencies in cancer. Specifically, the successful candidate will be proficient in (or able to learn and adapt to) data analysis, integration, and visualization across diverse -omics platform types (including cutting-edge technologies, such as single-cell and long-read RNA sequencing), and have a strong track-record of contributing to and driving relevant genomic research as evidenced by high-impact publications.

The position is full-time and the candidate is expected to work remotely, but must be located in Canada. Specifically the position can be setup as either fixed-term contract (with renewal) or potentially a permanent FTE position depending on the candidate’s background and preference.

• Analyze and integrate various clinical and -omics data from public repositories (DepMap, TGCA, ENCODE, SRA, etc.)
• Develop or deploy new methods as necessary to utilize new technologies
• Employ ML/AI methods to draw preclinical and molecular insights from omics data
• Prioritize results based on biological evidence from various resources
• Present research findings and contribute to publications/manuscript preparation

Core Job Requirements:

• M.S. or Ph.D. (Ph.D. strongly preferred) in computational biology or bioinformatics, or a related field.
• Fluency in both Python and R – statistical data analysis, data wrangling (with R:tidyverse and Python:pandas), data visualization (R:ggplot2, and Python:matplotlib/seaborn)
• Experience with analyzing bulk-omics data (e.g RNA-seq, Exome, ChIP-seq, ATAC-seq, etc.)
• Excellent oral and written presentation skills, creative problem-solving abilities, attention to detail, good communication and collaboration skills are expected.

Preferred skills and experience:

• Experience with traditional machine learning models and deep learning frameworks (using scikit-learn and PyTorch)
• Experience analyzing single-cell transcriptomics data
• Experience contributing to and driving relevant genomic research as evidenced by high-impact publications