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# Director, Data Science Operations - Early Cardiovascular, Renal and Metabolism

- **Company:** AstraZeneca plc
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $181,570.0 - $272,355.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Access, Artificial Intelligence, Data Analysis, Clinical Data Repository, Cloud Database, Code Review, Data Governance, Data Sharing, Metadata, DataOps, Software Engineering, Data Storage Technologies, Agentic-AI, Information Technology, Data Management, Data Pipelines
- **Published:** October 22, 2026
- **Apply:** https://astrazeneca.eightfold.ai/careers/job/563877691742671?domain=astrazeneca.com

## About the Role

* PhD in a quantitative or life science discipline, or equivalent experience.

 * 10+ years of experience in scientific data analysis, bioinformatics, or informatics.

 * 5+ years of experience leading interdisciplinary scientific, software engineering and data teams, with demonstrated accountability for outcomes delivered across multiple teams, functions and geographies in a matrix organization.

 * Experience leading the development and operation of data platforms for internal and external user groups. Track record of coordinating data collection, curation, harmonization, and processing

 * Advanced program and project management capabilities, including establishing agile, iterative delivery practices for scientific and technical teams.

 * Hands-on understanding of multi-omics and clinical data, processing pipelines, metadata and data quality, and cloud data environments.

 * Demonstrated experience using AI to optimize team workflows and delivery, including the orchestration of agentic AI solutions.

 * Experience developing and applying data governance, data sharing and access policies for research and patient data.

 * Demonstrated ability to strategically influence senior cross-functional stakeholders and to translate between scientific, clinical, data and technology teams

 * Ability to operate with autonomy and navigate ambiguity

## Description

* Set the operational model and data capabilities for the Early CVRM Data Science & AI, translating it into a value-led portfolio of priorities, delivery plans and measurable scientific outcomes.

 * Own the integrated portfolio view delivery risk and capacity to shape resource trade-offs and sequencing across projects.

 * Establish cross-functional governance and ways of working that accelerate high-quality delivery, resolve dependencies and maximize portfolio value across sites and organizational boundaries.

 * Anticipate and resolve delivery constraints, escalating material trade-offs and guiding timely decisions that protect scientific impact, quality and speed.

Team leadership

 * Mentor, inspire and develop a globally distributed, interdisciplinary team of data scientists and informaticians, while providing matrix leadership across partner teams and sites to ensure collective accountability for delivery.

 * Establish and lead a scalable, agile multi-site operating model with clear decision rights, accountabilities, priorities and capacity visibility, continuously improving productivity and performance across Data Science & AI and matrix teams.

 * Champion professional standards and best practices such as code review, reproducible workflows and documentation.

Stakeholder partnership

 * Build trusted partnerships with project teams and functional leaders across Early CVRM and R&D to align demand with portfolio priorities, focus resources on the highest-value scientific opportunities and engage data science at the point of greatest impact.

 * Provide strategic counsel and evidence-based recommendations to the Executive Director and Early CVRM leadership on portfolio priorities, investment choices, resource trade-offs and enterprise-level dependencies.

Data operations

 * Lead the implementation of data management initiatives, including data storage, metadata, cataloguing and access, in partnership with central IT, the R&D Data Office and data owners.

 * Translate scientific needs into data requirements and working processes for bringing in, documenting and accessing data from trials, collaborations and other sources.

Technology and AI

 * Define and own the technology and AI adoption strategy, prioritizing scalable capabilities that increase productivity, accelerate high-quality decisions and amplify scientific impact across the portfolio.

 * Provide senior technical direction and selective expert oversight as well as contribute hands-on to data workflows, pipelines and analyses where it accelerates delivery.