Bioinformatics Scientist/Sr. Scientist - Translational Biomarkers
Arcus Biosciences is an exciting young company founded on the vision of creating new cancer therapeutics through the utilization of unexploited insights in immunology. The company was formed in 2015 by a group of seasoned researchers from the biotechnology and pharmaceutical industries. We are located in the San Francisco bay area, in the heart of the world’s largest biotechnology research hub. Arcus Biosciences offers a competitive compensation and benefits package, including aggressive participation in the growth of the company in the form of stock option grants. Arcus is an ambitious undertaking, and we fully expect our company to become a force in the discovery, development and commercialization of novel therapies for the treatment of cancer.
Scientists at Arcus work in a highly embedded and highly collaborative model with colleagues across the organization. We seek a highly motivated Bioinformatics Scientist to work closely with translational and biology research scientists in the development of novel therapeutics and biomarkers in the immuno-oncology area. Candidates will be expected to work effectively on highly technical interdisciplinary teams and be driven to pursue creative solutions to challenging problems. The successful applicant will be expected to take the lead in providing Bioinformatics expertise to cross functional project teams working on drug discovery and biomarker development.
- A Ph.D +/- postdoc in Bioinformatics, Biostatistics, Computational Biology, Computer Science or similar field, ideally in the field of cancer/immunology and/or immuno-oncology.
- Minimum of 2 year experience in industry.
- Experience in predictive/prognostic biomarkers and translational research with applications in patient datasets and/or clinical trials.
- Proven coding expertise in languages such as R and/or Python for bioinformatics analyses.
- Expertise in NGS analyses such as RNAseq or exomeseq and survival/outcome analyses.
- Strong understanding of oncology and/or immunology concepts and applications.
- Expertise working with large scale genomic data (e.g. ctDNA, TCRseq, single cell assays, microarrays, flow cytometry, ATACseq, CHIPseq, etc.) or proteomics and immunohistochemistry data would be a significant plus.
- Experience with machine learning and deep learning algorithms would be a significant plus.
- Demonstrated ability to present complex results, both verbally and in writing, to bioinformatics and non-bioinformatics audiences of all levels is critical.
- Comfortable working in a matrix environment and handling several concurrent fast paced projects.