AstraZeneca

AstraZeneca

Director, Clinical AI Delivery Operations

US - Gaithersburg - MD · Director

Sponsorship not specified$186k-$279kDetected 2 days ago
PythonMachine LearningData EngineeringData ScienceLLMsAgentic AIMLOpsJiraConfluenceClinical TrialsBioinformaticsRadiologyResearchCommunicationCollaborationProblem SolvingGxP

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odds of building a lasting career here

64Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

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About the role

  • About AISI AI Strategy & Innovation (AISI) sits at the centre of AstraZeneca's R&D AI transformation.
  • 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.

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 approach to data and model governance and quality. This covers dataset versioning, lineage and traceability, fit-for-purpose criteria, and quality control.
  • Support audit and inspection readiness for the team's data and AI workflows.
  • Maintain consistency, traceability, and usability of datasets across projects.
  • 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.
  • Build in the 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 closely with AI scientists, ML engineers, data scientists, program managers, clinical and regulatory business partners, IT and data teams.

Requirements

  • 5 years of experience managing complex scientific or clinical datasets, including multimodal data.
  • Experience with dataset versioning, reproducible data preparation, and quality control.
  • Experience with MLOps, data pipelines, or model lifecycle documentation.
  • Working knowledge of cloud-based data environments, modern data platforms, and dataset management workflows.
  • 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.
  • Familiarity with generative AI or agentic systems in clinical or regulatory settings.
  • Knowledge of clinical and regulatory data standards (e.g., CDISC SDTM/ADaM, eCTD, DICOM) and of GxP computerized system validation.
  • Working knowledge of Atlassian tools (JIRA, Confluence).

Nice to have

  • Experience defining or owning data and/or model management processes, standards, or governance frameworks, ideally in a regulated environment.

Skills

  • Excellent interpersonal, verbal, and written communication skills in an international matrix environment.

Compensation

  • The annual base pay for this position ranges from $186,014.40 - $279,021.60 USD Annual [80% - 120% ].

Benefits

  • offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.

Equal opportunity

  • If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.

This listing is sourced directly from AstraZeneca's careers page and normalized into a canonical job model.