Senior Scientist, Computational Oncology

Job Description

Senior Scientist, Computational Oncology

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 …