Senior Specialist, Data Science — Operational Data Strategy (ODS)

The Operational Data Strategy (ODS) function provides strategic oversightforhow 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. ODSisa key division within R&D at AstraZeneca that partners across BioPharma to elevateevidencegeneration and operational excellence.

We are seeking aSenior 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 traitscritical thinking growth mindset grit and resiliencewhile coaching specialists and raising the quality bar across ODS.

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 whenappropriate.
• Respond to ad hoc queries from senior stakeholders withtimelyaccurateanalytical outputs and clearly articulated assumptions and limitations.
• Frame core issues develop and refine hypotheses and design strategic analytics plans aligned to program and portfolioobjectivesin clinical operations.
• Identifyand evaluate relevant primary and secondary sources; synthesize quantitative and qualitative insights across multiple systems and datasets.
• Provideexpertisein exploratory descriptive and predictive analytics; design implement and evaluate machine learning models for classification regression clustering and time-to-event problems asappropriate.
• Maintain high quality standards under pressure enforcing quality reviews source assessment and alignment to hypotheses to avoid nonvalue-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 effectivelyonETL 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 ensureutilizationand value of ODS analytics; provide training and advice onoptimaluse of key data and analyses.
• Practice strong upward management withtimely comprehensive progress reporting; own workstreams end-to-end from hypothesis to presentation; guide others to do the same.
• Model key leadership traitsintegrity commitment initiative personable engagement adaptability organization time consciousness creativity and strategic thinkingand mentor others to adopt them.

Education QualificationsSkillsand Experience Essential:

• Bachelors degree in computer science data analysis statistics engineering ora relateddiscipline (or equivalent experience).
• Masters degree in computer science data analysis statistics applied mathematics ora relevantdiscipline (or equivalent 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 modernMLOpspractices.
• Evidence of process improvement and standard setting across analytics workflows model governance and stakeholder adoption.