Computational Biologist #job

Job Location
Redwood City, CA
Job Description

Oncology is a key therapeutic area for AbbVie, with a portfolio consisting of three marketed products - Imbruvica, Venclexta, and Empliciti - and a pipeline containing multiple promising new molecules that are being studied in nearly 200 clinical trials in 19 different types of cancer. 
AbbVie is expanding its oncology hub on the West Coast, with three sites in the San Francisco Bay Area (Redwood City, South San Francisco, and Sunnyvale) focused on the discovery and development of novel oncology therapies. More than 1,000 AbbVie scientists, clinicians, and product developers with strong entrepreneurial roots work across these three sites. They combine their expertise in immuno-oncology, stem cells, and cell-signaling with their knowledge of bispecific antibodies, antibody-drug conjugates (ADCs), and covalent-inhibitor technologies to discover and develop novel cancer treatments. 
The successful candidate will provide computational biology support to a team of highly skilled immune-oncology scientists and interact with peers across a highly collaborative and science-driven organization to create, investigate and characterize novel therapeutics. This will involve formulating and answering biological questions using complex datasets and the creation and assessment of novel computational solutions. You will work in a well-supported environment, with access to the tools, technologies and expertise necessary for success. 
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 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


Candidate's title and level will be based on level of experience 

• 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. 


• 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. 

• Familiarity with OmicSoft / Array Studio software. 

• 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

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