AstraZeneca is a global, science-driven biopharmaceutical company. We are dedicated to discovering, developing, and delivering innovative, meaningful medicines and healthcare solutions that enrich the lives of patients. The vision of AstraZeneca Oncology is to redefine cancer, redefine our solutions to cancer, and restore patients' lives.
The Early Computational Oncology team works with our oncology drug project teams throughout the research and early development pipeline, from new target discovery to translational medicine and clinical trials. Our global group aligns its activities to AstraZeneca's primary areas of focus in Oncology: immuno-oncology, DNA damage response, tumour drivers & resistance, epigenetics, cell therapies and antibody-drug conjugates.
Excitingly the team is growing to meet the challenges of pre-clinical and clinical big data generation initiatives in AZ Oncology including DNA sequencing, single-cell and bulk expression, proteomics and CRISPR. We have multiple opportunities available for talented and motivated bioinformaticians. Positions include:
Senior scientist for discovery and portfolio support for immuno-oncology (All sites).
Senior scientist for discovery and portfolio support in cell therapy and antibody-drug conjugates (All sites).
Senior scientist for discovery and portfolio support in epigenetics (Boston preferred).
Senior scientist knowledge graph team driving new insight for immuno-oncology (Gaithersburg preferred).
Main Duties and Responsibilities:
Work as part of drug project and clinical teams to understand their scientific and technical challenges and proactively impact these using bioinformatics.
Apply data and knowledge analytics to deliver actionable insight driving discovery and development of the next generation of cancer medicines.
Discover, design and code innovative ways to identify and visualise meaningful patterns in complex data.
Work with our labs to influence generation of data assets.
Share code and train peers and bench scientists in the tools critical to understanding complex data.