Job Description
Our company in the United States and Canada, is a global health care leader with a diversified portfolio of prescription medicines, vaccines and animal health products. The difference between potential and achievement lies in the spark that fuels innovation and inventiveness; this is the space where our company has codified its legacy for over a century. our company's success is backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.
Our company is on a quest for cures and is committed to being the world’s premier, most research-intensive biopharmaceutical company. Today, we’re doubling down on this goal. Our company's Research Laboratories is a true scientific research facility of tomorrow and will take our company's leading discovery capabilities and world-class small molecule and biologics R&D expertise to create breakthrough science that radically changes the way we approach serious diseases.
The Data, AI, and Genome Sciences department is looking for a passionate and talented computational biologist to join our Translational Genome Analytics research team based in South San Francisco, CA. In this role, you will design and apply systematic machine learning and network-based approaches to elucidate molecular mechanisms of disease progression and drug response to drive target discovery and drug development efforts to impact our rapidly growing oncology portfolio. You will have the opportunity to collaborate with cross-functional teams of computational biologists, data scientists and bench scientists in Discovery Oncology.
Oncology research at our company is driven by a deep interest in the biology of tumor and its microenvironment, and how diverse points of intervention can be combined to achieve ever higher rates of durable response and patient overall survival.
In this exciting role, you will:
• Contribute to multiple stages of Oncology drug discovery to decode genetic dependencies and identify targetable cell-surface antigens by interrogating high-throughput assays, including genomics, transcriptomics and proteomics datasets.
• Leverage cutting-edge AI/ML and network-based approaches to elucidate multiscale cellular and disease mechanisms underlying drug response and resistance
• Integrate large internal and public biological data sets including Next Generation Sequencing (NGS) data (e.g. RNA-Seq, Perturb-Seq, single cell RNA-Seq, WGS, CRISPR) as well as rich compound screening data (e.g. PRISM, Sanger, MIX-seq) to inform target prioritization and drug combinations
• Collaborate with experimental scientists across functions to characterize novel targets coming from genetics, translational and disease pathway exploration, explore target engagement, decode mechanisms of action of drugs, and provide functional validation of novel drug targets.
• Be proactive and work collaboratively across disciplines, including molecular biologists, protein scientists, bioinformaticians, and software engineers
• Employ best reproducible research and data integrity practices to generate reusable analysis frameworks and reports to support Discovery Oncology target identification and validation efforts.
Education Minimum Requirement:
• Ph.D. in Bioinformatics, Biostatistics, Computational biology, Computer Science, Genetics, Immunology, Mathematics, Molecular Biology, Statistics or related field.
Required Experience and Skills:
• Passion to solve biological problems and identify problems that can be efficiently solved through computational methods and algorithms
• Experience with computational analysis and biological interpretation of diverse large-scale NGS experimental datasets
• Understanding the pros and cons of various algorithms for DNA-seq, RNA-seq, single-cell RNA-seq and/or functional genomics data
• Previous experience with experimental design of Oncology biological assays, statistical hypothesis testing, and biological interpretation
• Proficiency in at least one statistical programming language, such as R or Python
• Familiarity with public databases, and repositories of DNA, RNA and single cell profiling data, e.g. The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Dependency Map (DepMap).
• Skilled at integrating results generated from multiple omics data sources, and biological knowledge bases to customize analytical approaches for discovery research
• Interest in identifying novel applications of AI / machine learning strategies for biological target discovery
• Experience with AWS cloud computing infrastructure (e.g., S3, EFS, EC2, etc) and Linux environments.
• Experience with version control environments, such as Git
• Demonstrate the ability to learn, be proactive and motivated, and consistently focus on details and execution
• Excellent oral and written communication skills
Preferred Experience and Skills:
• Strong background with a post-doctora
Location: South San Francisco, CA
Posted: Aug. 19, 2024, 7:40 p.m.
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