Director, Clinical AI Delivery Operations

AstraZeneca•Gaithersburg, MD

3d•$186,014 - $279,022•Hybrid

About The Position

AstraZeneca’s AI Strategy & Innovation (AISI) is building an AI for Clinical Development capability within the Enterprise AI Unit to accelerate clinical-trial design, conduct, and analysis. The role establishes and runs the operational and methodological foundation that connects clinical AI research with clinical development and patient impact. It ensures multimodal data, models, and AI outputs are fit for AI development, scientific and clinical decision-making, and regulatory use, with delivery, regulatory, compliance, and quality standards built into projects from the outset. The position develops team-wide operating processes and governance standards for consistent, auditable, and scalable delivery. It works across AI science and engineering, clinical and regulatory functions, IT, data partners, and quality partners. The team is being built from the ground up, and the role suits an organized, dependable, data-driven, detail-oriented professional motivated to translate emerging AI research into governed, compliant methods for regulated clinical development and operations. The role covers governance and quality standards; data access, transfer, curation, and reproducible preparation; operational delivery and handover of clinical AI work; regulatory compliance, documentation, and audit readiness; cross-functional coordination; and continuous improvement. The position is based on an average expectation of at least three office days per week, with flexibility. Annual base pay is $186,014.40–$279,021.60 USD, with eligibility for incentives. Benefits include qualified retirement programs, paid time off, and health, dental, and vision coverage under applicable plan terms.

Requirements

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, ideally in a regulated environment.
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 ability, especially when diagnosing data-quality or workflow issues.

Nice To Haves

Bachelor’s or Master’s degree in data science, bioinformatics, health or biomedical informatics, or a related field.
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 the team’s data and model governance and quality approach, including dataset versioning, lineage, traceability, fit-for-purpose criteria, and quality control.
Serve as the contact for governance, compliance, and quality questions and keep the team aligned with evolving internal policies and external expectations.
Establish and report metrics for delivery performance, data and model quality, and compliance to team leadership.
Support audit and inspection readiness for data and AI workflows.
Coordinate with enterprise data owners, clinical data management, IT, and external collaborators to access and transfer multimodal data, models, and 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 dataset completeness, accuracy, and quality in line with SOPs, maintaining 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 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 expected 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.
Work 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 decisions.
Identify and implement improvements to workflows, tooling, and processes so delivery can scale as the team grows.
Stay informed about emerging data and model management practices, clinical and multimodal data and model standards, and AI governance expectations.