Senior Specialist, Data Science - Operational Data Strategy (ODS), BioPharma, AstraZenecaSection 1: Overview of the RoleThe Operational Data Strategy (ODS) function provides strategic oversight for how clinical operations data is collected, organized, validated, and analyzed across R&D. ODS combines advanced database and system capabilities with innovative data science methodologies to enable visual, data-driven decision making in clinical operations. ODS is a key division within R&D at AstraZeneca that partners across BioPharma to elevate evidence generation and operational excellence.
We are seeking a Senior Specialist, Data Science to be a key asset within ODS, reporting to the Strategic Analytics and Enablement Lead. You will drive complex analytics programs, design and implement predictive models, and translate business needs into rigorous data science solutions that create tangible impact in clinical operations. Core deliverables emphasize advanced analytics outputs and AI/ML applications; dashboards in Power BI are supportive rather than central. You will embody our core traits-critical thinking, growth mindset, grit, and resilience-while coaching specialists and raising the quality bar across ODS.
Section 2: Typical Accountabilities- Coordinate the implementation of analytical and data visualization solutions across clinical operations, ensuring scalability, reproducibility, and clear governance.
- Develop solutions to business and analytics challenges using established frameworks and tools, translating complex operational needs into robust data science deliverables.
- Lead advanced analytics and visualization approaches that enable data-driven decision making; use dashboards as communication aids when appropriate.
- Respond to ad hoc queries from senior stakeholders with timely, accurate analytical outputs and clearly articulated assumptions and limitations.
- Frame core issues, develop and refine hypotheses, and design strategic analytics plans aligned to program and portfolio objectives in clinical operations.
- Identify and evaluate relevant primary and secondary sources; synthesize quantitative and qualitative insights across multiple systems and datasets.
- Provide expertise in exploratory, descriptive, and predictive analytics; design, implement, and evaluate machine learning models for classification, regression, clustering, and time-to-event problems as appropriate.
- Maintain high quality standards under pressure, enforcing quality reviews, source assessment, and alignment to hypotheses to avoid non-value-add analysis.
- Keep solutions at the leading edge by developing and applying ongoing knowledge of analytics trends, methodologies, and tools; contribute to the definition of ODS standards and best practices.
- Define and guide best practices for data collection and preprocessing across databases, APIs, and files; partner effectively on ETL and data engineering handoffs.
- Compile insights into figures, charts, and tables and craft concise narratives with strong vertical and horizontal logic for executive decision forums.
- Present complex work to principals and cross-functional stakeholders; engage dynamically with feedback and tailor content to varied audiences; coach specialists on effective communication.
- Build and manage effective relationships to ensure utilization and value of ODS analytics; provide training and advice on optimal use of key data and analyses.
- Practice strong upward management with timely, comprehensive progress reporting; own workstreams end-to-end from hypothesis to presentation; guide others to do the same.
- Model key leadership traits-integrity, commitment, initiative, personable engagement, adaptability, organization, time consciousness, creativity, and strategic thinking-and mentor others to adopt them.
Section 3: Education, Qualifications, Skills and Experience Essential- Bachelor's degree in computer science, data analysis, statistics, engineering, or a related discipline, and 4+ years of experience.
- Master's degree in computer science, data analysis, statistics, applied mathematics, or a relevant discipline, and 4+ years of experience.
- PhD in computer science, data analysis, statistics, applied mathematics, or a relevant discipline, and 1+ year of experience
- Demonstrated expert knowledge of analytics and visualization tools such as Python, Power BI, and Spotfire, with emphasis on delivering advanced analytics outputs over dashboards.
- Familiarity with database systems (SQL and NoSQL), ETL pipelines, cloud environments, and software development best practices, including reproducibility and version control.
- Demonstrated experience developing complex data analyses in business and scientific domains, including Clinical Operations.
- Excellent written and verbal communication skills in English, with the ability to clearly communicate uncertainties, assumptions, and limitations.
- Strong understanding of data science principles, machine learning algorithms (classification, regression, clustering), statistical inference, and model evaluation methodologies.
Desired- Experience working in Agile delivery environments and exposure to modern MLOps practices.
- Evidence of process improvement and standard setting across analytics workflows, model governance, and stakeholder adoption.
Core Traits and Why They Are Critical Success Factors- Critical Thinking: A deep, structured approach to problem solving enables precise problem framing, sound method selection, and unbiased interpretation-vital for transforming operational data into decisions that impact study timelines, quality, and cost.
- Growth Mindset: Curiosity and a learning orientation ensure rapid adoption of new AI/ML techniques, data sources, and evolving business needs, keeping ODS solutions modern, scalable, and impactful across R&D.
- Grit: Perseverance sustains momentum through complex data ecosystems, regulatory constraints, and cross-functional dependencies; it underpins delivery on long-running initiatives and in ambiguous contexts.
- Resilience: The capacity to recover from challenges maintains performance under pressure, enables constructive responses to feedback, and fosters an evidence-first culture essential in high-stakes clinical environments.
Section 4: Key Relationships to Reach Solutions Internal- Clinical Data and Insights Leadership Team; Biopharmaceuticals Clinical Operations Leadership Team and BPOs; senior leaders and peers in other R&D functions; HR, Finance, Global Business Services, IT, Procurement, and other enabling functions.
External- Clinical Research Organizations and other outsourcing providers; external service providers; benchmarking organizations.
Date Posted25-Jun-2026
Closing Date10-Jul-2026
