Director, Clinical AI Delivery Operations

AI Strategy & Innovation (AISI) sits at the centre of AstraZeneca's R&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science, biomarkers and clinical development.

AstraZeneca is building a world-class AI for Clinical Development capability within the Enterprise AI Unit. It will accelerate the design, conduct, and analysis of clinical trials across our enterprise pipeline. We are seeking an AI Senior Specialist, Clinical AI Delivery Operations, to build and run the operational and method delivery foundation that connects clinical AI research with clinical development and patient impact.

You will make sure that multimodal data, models, and AI outputs are fit for purpose for AI development, scientific and clinical decision-making, and regulatory use. You will also make sure that delivery, regulatory, compliance, and quality standards are met from the start of each project.

You will also develop the team-wide operating processes and governance standards that make AI system and output delivery consistent, auditable, and scalable. You will work with AI scientists and engineers, clinical and regulatory domain experts, IT, data partners, and quality partners.

This team is being built from the ground up. The ideal candidate is organized, dependable, data-driven, and detail-oriented. They are motivated by translating advanced and emerging AI research into governed, compliant methods and processes designed for the highly regulated clinical development and operations settings.

• Define, implement, and maintain team-wide standards, SOPs, templates, and checklists for data and model intake, curation, dataset release, documentation, and handover.
• Own the team's approach to data and model governance and quality. This covers dataset versioning, lineage and traceability, fit-for-purpose criteria, and quality control.
• Serve as the point of contact for governance, compliance, and quality questions. Keep the team aligned with internal policies and external expectations as they evolve.
• Establish metrics for delivery performance, data and model quality, and compliance, and report on them to team leadership.
• Support audit and inspection readiness for the team's data and AI workflows.

• Coordinate with enterprise data owners, clinical data management, IT, and external collaborators to access and transfer multimodal data, models, and associated metadata.
• Ensure datasets and models meet defined formats, metadata standards, and project requirements for each modality.
• Translate scientific and model development requirements into structured dataset preparation tasks.
• Verify the completeness, accuracy, and quality of datasets in line with SOPs.
• Maintain consistency, traceability, and usability of datasets across projects.

• Run and coordinate the operational workflows that take clinical AI work from research to evidence-backed output. This includes data pooling, subsetting and splitting, annotation consolidation, and reproducible data preparation.
• Support handover of validated datasets, models, and outputs to clinical, regulatory, engineering, and IT partners.
• Manage storage and release of results in approved data environments.

• Ensure datasets and workflows comply with internal policies and governance frameworks. Examples are data privacy, human biological sample governance, and data access approvals (e.g., iDAP).
• Initiate and coordinate approvals with the Data Enablement, Quality, IT, and Compliance teams.
• Build in the data integrity and audit-readiness…