Senior Scientist I/II, Computational Biology

AbbVie Biotherapeutics, a center of innovation in oncology R&D located in Redwood City, California, is seeking a computational biology/bioinformatics scientist to play an integral role in a multi-disciplinary team focused on developing new therapies for the treatment of cancer. AbbVie has a bioinformatics program which spans multiple locations and therapeutic areas. This enables our capabilities to be leveraged from the perspective of a research community: a shared code base, innovative project engagement, and an extensive mentoring and development program that combine to create a vibrant and exciting career opportunity.

The successful candidate will provide computational biology support to a team of highly skilled immune-oncology scientists and interact with peers across a very collaborative and science-driven organization to create, investigate and characterize novel therapeutics. This will involve formulating and answering biological questions using complex datasets, large population cohorts, and our own clinical trial data to create and assess novel computational solutions, while contributing to novel discoveries and helping to drive the best programs forward in the clinic.

Key Responsibilities

• Analysis - Interrogate large scale -omics data from both pre-clinical models and clinical samples in support of new therapeutic discoveries for the treatment of cancer.
• Build - through your own efforts or by working with software engineers to develop world-class bioinformatics capabilities (genomic databases and analytics software/pipelines) to support a dynamic oncology pipeline.
• Collaborative Design - you should find it inspiring to work with a wide variety of colleagues (e.g., biology, chemistry, business); understand their needs and then propose and build solutions
• Presentation - use your strong computational and data visualization skills to bring together disparate data types in compelling visualization packages that provide end users with the ability to see both 'the landscape' and details leading to improved decision-making
• Adaptability - given our diverse mission you should be able to move fluidly between different project responsibilities
• Breadth - as a talented and motivated Biology Data Scientist you can contribute to a wide variety of missions related to enhancing drug discovery

• Bachelor's degree and typically 10 years of experience; or a Master's degree and typically 8 years of experience, or a PhD. Degree should be in Biology, computational biology, bioinformatics, or similar field with a thorough theoretical and practical understanding of this field.

Preferred:

• Several years of experience and demonstrated skills/accomplishments in an immune-oncology/oncology setting is highly desired
• Expertise in computational biology/bioinformatics, genomic analysis, data and literature mining, disease and biological pathway and gene network/ systems biology analysis.
• Strong data analytical skills, expertise in various bioinformatics software, databases, and programming packages including R, Shiny, Python or Perl.
• Understand how to use and leverage containers for deploying standard pipelines (Docker, AWS Container, etc.)
• Familiarity with running standard pipelines and generating QC reports on one or more of NGS based technologies (RNA-Seq, single cell RNA-Seq, DNA-Seq for Genome/Exome sequencing, ATAC-Seq), Proteomics technologies (Mass Spec based Proteomics), Methylation, Somatic Variant Calling and annotation, Targeted Mutational Profiling, Copy Number Variation, and standard microarray based technologies (Affymetrix, Illumina)
• Good grasp of biochemistry, immunology, cancer biology and related subjects.
• Strong communication skills with people from different academic backgrounds.
• Experience with biomarker strategies for drug development in areas including -omics, immuno-oncology, is valued.
• Builds strong relationships with peers and cross functionally with partners outside of team to enable higher performance
• Learns fast, grasps the 'essence' and can change the course quickly where indicated
• Raises the bar and is never satisfied with the status quo
• Creates a learning environment, open to suggestions and experimentation for improvement