Director, Clinical AI Delivery Operations

AstraZeneca•Gaithersburg, MD

1d•Hybrid

About The Position

AstraZeneca's AISI AI Strategy & Innovation team is at the centre of the company's R&D AI transformation, building, buying, and delivering AI models and agents across discovery, translational science, biomarkers, and clinical development. The Enterprise AI Unit is building an AI for Clinical Development capability to accelerate the design, conduct, and analysis of clinical trials across its pipeline. The Clinical AI Delivery Operations role builds and runs the operational and methodological foundation connecting clinical AI research with clinical development and patient impact. The role ensures multimodal data, models, and AI outputs are fit for AI development, scientific and clinical decision-making, and regulatory use, while meeting delivery, regulatory, compliance, and quality standards from project outset. It also develops team-wide operating processes and governance standards so delivery is consistent, auditable, and scalable. The role works with AI scientists and engineers, clinical and regulatory experts, IT, data partners, and quality partners in a team being built from the ground up.

Requirements

Bachelor's or Master's degree in data science, bioinformatics, health or biomedical informatics, or a related field.
At least 5 years of experience managing complex scientific or clinical datasets, including multimodal data.
Experience defining or owning data and/or model management processes, standards, or governance frameworks; experience in a regulated environment is preferred.
Fluency with FAIR data principles and their application in life sciences.
Working knowledge of cloud-based data environments, modern data platforms, and dataset-management workflows.
Understanding of AI/ML data requirements for model development, validation, and monitoring.
Experience with dataset versioning, reproducible data preparation, and quality control.
Working knowledge of Python or a similar scripting language for data preparation, automation, and QC.
Familiarity with data governance, compliance processes, and controlled handling of protected data.
Proven ability to coordinate delivery across multiple stakeholders and workstreams.
Excellent interpersonal, verbal, and written communication skills in an international matrix environment.
Ability to influence without authority and gain adoption of standards across scientific and engineering teams.
Strong execution mindset, with ownership and accountability for high-quality delivery.
Highly structured and detail-oriented, with strong follow-through.
Pragmatic, fit-for-purpose problem-solving skills, especially when diagnosing data-quality or workflow issues.

Nice To Haves

Experience in clinical data management, life sciences, or healthcare.
Familiarity with generative AI or agentic systems in clinical or regulatory settings.
Experience with MLOps, data pipelines, or model lifecycle documentation.
Knowledge of clinical and regulatory data standards such as CDISC SDTM/ADaM, eCTD, and DICOM.
Knowledge of GxP computerized system validation.
Experience supporting audits or inspections.
Working knowledge of Atlassian tools such as JIRA and Confluence.

Responsibilities

Define, implement, and maintain team-wide standards, SOPs, templates, and checklists for data and model intake, curation, dataset release, documentation, and handover.
Own data and model governance and quality practices, including dataset versioning, lineage, traceability, fit-for-purpose criteria, and quality control.
Serve as the point of contact for governance, compliance, and quality questions and keep the team aligned with evolving internal policies and external expectations.
Establish delivery performance, data and model quality, and compliance metrics, and report them to team leadership.
Support audit and inspection readiness for the team's data and AI workflows.
Coordinate access to and transfer of multimodal data, models, and associated metadata with enterprise data owners, clinical data management, IT, and external collaborators.
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 and verify dataset completeness, accuracy, and quality in line with SOPs.
Maintain dataset consistency, traceability, and usability across projects.
Run and coordinate operational workflows that take clinical AI work from research to evidence-backed output, including 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, including requirements concerning data privacy, human biological sample governance, and data access approvals such as iDAP.
Initiate and coordinate approvals with Data Enablement, Quality, IT, and Compliance teams.
Build in data integrity and audit readiness for regulated work.
Author and maintain documentation such as data collection plans, data and model lineage records, analysis plans, and reports.
Manage QC checks, deviations, and issue resolution.
Collaborate with AI scientists, ML engineers, data scientists, program managers, clinical and regulatory business partners, and IT and data teams.
Communicate data and model readiness, limitations, and uncertainties clearly to support decision-making.
Identify and implement workflow, tooling, and process improvements to scale delivery as the team grows.
Stay informed about emerging data and model management practices, clinical and multimodal data and model standards, and AI governance expectations.