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. Position title would be commensurate with years of relevant experience.
- A PhD or PhD plus postdoc in Bioinformatics, Biostatistics, Computational Biology, Computer Science or similar field, ideally in the field of cancer/immunology and/or immuno-oncology.
- Expertise working with gene expression data such as microarray and/or RNAseq in human or mouse datasets.
- Proven coding expertise in languages such as R or Python for bioinformatics analyses.
- Strong understanding of oncology and/or immunology concepts and applications.
- 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.
- Expertise working with large scale genomic data (e.g. TCRseq/BCRseq, single cell assays, flow cytometry, ATACseq, CHIPseq) or proteomics/immunohistochemistry data would be a significant plus.
- Experience with drug treatment or perturbation datasets such as siRNA/CRISPR library screens would be preferred.
- Prior experience with neoantigen prediction and ctDNA analysis algorithms would be a distinct advantage.
- Experience with RNAseq and exomeSeq pipelines and prior experience in Amazon Web Services (AWS) or high performance computing environment would be preferable.
- Experience with machine learning and deep learning algorithms would be a significant plus.
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