Associate Director, Digital Proteomics and AI Innovation

Make a meaningful impact on patients’ lives around the world At AstraZeneca, we are combining cutting-edge science, data and artificial intelligence to transform how we discover and develop medicines. This role sits within the Centre for Genomics Research (CGR) and works in close collaboration with AstraZeneca Infectious Disease and Oncology, offering a unique opportunity to shape new digital and AI capabilities for the cancer vaccine space. Based in CGR, you will sit at the intersection of mass spectrometry-based proteomics, immunopeptidomics and agentic AI — building the data foundations and intelligent systems that allow scientists to explore high-throughput proteomics, HLA- peptidomics , neoantigen discovery, genetic sequencing, transcriptomics and emerging molecular assays as a connected whole. This is a hands-on technical role for someone who can combine deep proteomics expertise with the practical engineering judgement needed to design, deploy and scale AI systems that scientific users actually rely on. What you'll do As Associate Director, Digital Proteomics and AI Innovation within CGR, you will: Identify high-value workflow opportunities where agentic AI can transform how proteomic, peptidomic and multi- omic data are explored, integrated, interpreted and automated. Design, build, deploy and iterate AI-enabled systems — including agentic workflows — that are usable, maintainable and integrated into real scientific practice. Bring deep domain expertise in mass spectrometry, proteomics and peptidomics to shape data standards, analytical approaches and biological interpretation, with particular relevance to immunopeptidomics and neoantigen-centric workflows. Drive integrative analysis across proteomics, peptidomics , sequencing, transcriptomics and experimental metadata to generate translational insight. Partner with informaticians, data and mass spectrometry scientists within CGR, and collaborate closely with scientific stakeholders in AstraZeneca Infectious Disease and Oncology, to embed AI capabilities into scalable, governed analytical environments. Contribute technical expertise to scientific discussions, sharing knowledge with colleagues and engaging with internal and external scientific communities. Essential criteria Advanced degree (PhD or equivalent experience) in proteomics, computational biology, bioinformatics, data science, biostatistics, computer science or a related discipline. Deep expertise in mass spectrometry-based proteomics and peptidomics , including a working understanding of experimental design, data characteristics, limitations and interpretation challenges. Strong hands-on data science and software delivery experience, with a track record of building, deploying and maintaining analytical or AI-enabled systems used by scientific teams. Demonstrated experience developing agentic AI workflows or advanced AI systems for scientific data exploration, interpretation or workflow automation. Proven ability to integrate and analyse multidimensional datasets spanning proteomics, peptidomics , sequencing, transcriptomics and associated metadata to generate biological insight. Ability to operate as an independent, hands-on technical contributor — combining scientific judgement with pragmatic engineering and delivery. Excellent communication and collaboration skills, with a track record of translating complex scientific and technical concepts into solutions that scientific and technical users adopt. Desirable criteria Experience applying digital proteomics and AI-enabled approaches in oncology, immunology, cancer vaccines, immunopeptidomics or neoantigen discovery. Experience designing data foundations, ontologies, harmonised analytical environments or reusable AI capabilities for large-scale scientific research. Experience extending across additional omics modalities in partnership with domain specialists. Track record of innovation evidenced through deployed tools, software products, open-source contributions, peer-reviewed publications or conference presentations in proteomics, peptidomics , AI or data science. </li