Associate Director, Digital Proteomics and AI Innovation

AstraZeneca•Gaithersburg, MD

12d

About The Position

The Associate Director, Digital Proteomics and AI Innovation is based in AstraZeneca’s Centre for Genomics Research (CGR) and works closely with Infectious Disease and Oncology teams. The role combines mass spectrometry-based proteomics, immunopeptidomics and agentic AI to build data foundations and intelligent systems that help scientists explore and connect high-throughput proteomics, HLA-peptidomics, neoantigen discovery, genetic sequencing, transcriptomics and emerging molecular assays. It is a hands-on technical position focused on developing and scaling practical AI systems that scientific users can rely on, with particular relevance to the cancer vaccine space. The role identifies opportunities for agentic AI in scientific workflows; builds, deploys and iterates usable, maintainable AI-enabled systems; and applies proteomics and peptidomics expertise to data standards, analysis and biological interpretation. It integrates multidimensional datasets to generate translational insight and collaborates with CGR colleagues and scientific stakeholders across Infectious Disease and Oncology to embed AI capabilities in scalable, governed analytical environments. The postholder also contributes expertise to scientific discussions and shares knowledge with internal and external scientific communities.

Requirements

At least 3 years of experience and an advanced degree, PhD or equivalent experience.
Deep expertise in mass spectrometry-based proteomics and peptidomics, including understanding experimental design, data characteristics, limitations and interpretation challenges.
Strong hands-on data science and software delivery experience, including 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.
Ability to integrate and analyse multidimensional datasets spanning proteomics, peptidomics, sequencing, transcriptomics and associated metadata to generate biological insight.
Ability to work as an independent, hands-on technical contributor, combining scientific judgement with pragmatic engineering and delivery.
Excellent communication and collaboration skills, including the ability to translate complex scientific and technical concepts into solutions adopted by scientific and technical users.

Nice To Haves

PhD in proteomics, computational biology, bioinformatics, data science, biostatistics, computer science or a related discipline.
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 analysis across additional omics modalities in partnership with domain specialists.
Innovation demonstrated through deployed tools, software products, open-source contributions, peer-reviewed publications or conference presentations in proteomics, peptidomics, AI or data science.

Responsibilities

Identify high-value workflow opportunities where agentic AI can transform the exploration, integration, interpretation and automation of proteomic, peptidomic and multi-omic data.
Design, build, deploy and iterate usable, maintainable AI-enabled systems, including agentic workflows, integrated into scientific practice.
Apply mass spectrometry, proteomics and peptidomics expertise to data standards, analytical approaches and biological interpretation, particularly for immunopeptidomics and neoantigen-centric workflows.
Drive integrative analysis across proteomics, peptidomics, sequencing, transcriptomics and experimental metadata to generate translational insight.
Partner with CGR informaticians, data scientists and mass spectrometry scientists, and collaborate with stakeholders in Infectious Disease and Oncology to embed AI capabilities into scalable, governed analytical environments.
Contribute technical expertise to scientific discussions, share knowledge with colleagues, and engage with internal and external scientific communities.