Executive Director, Data Science and Bioinformatics (CVRM)

We harness digital, data science & AI to fast-forward our research. Making sure work born in a lab can make a real difference. Everyday, impacting patients’ lives across the world. 
 
About the role 

Provide global Therapy Area and Data and AI leadership and expertise spanning enterprise enablement and R&D bioinformatics within CVRM, bringing deep expertise in at least one domain and the ability to lead across a multidisciplinary ecosystem. 
 

Develop and deliver multi-year Data & AI strategies that accelerate target discovery, translational insights, biomarker strategies, and clinical development, while aligning with AZ’s cross-functional business agenda. 
 

Lead the conversion of multi-modal data (genomics, transcriptomics, proteomics, metabolomics, single-cell, imaging, EHR/RWD, wearable/digital biomarkers) into decision-grade insights, through multidisciplinary teams and scalable capabilities through multidisciplinary teams and scalable capabilities.   
 

Operates globally with enterprise scope; sit on the Early CVRM Leadership Team and contribute materially to Early CVRM global AI strategy 
 
Accountabilities 

• Global strategy and portfolio alignment: Define and implement long-term Data & AI strategies for Early CVRM R&D within an enterprise framework; ensure alignment with global cross-functional priorities, program goals, and portfolio governance forums. 
• R&D bioinformatics leadership: Lead the analytical approach from discovery through development—target identification/validation, disease segmentation, mechanistic and predictive modelling, biomarker discovery, and responder identification—linking insights to clinical trial optimization. 
• AI/ML advancement and governance: Champion responsible, fit-for-purpose AI/ML, Drive adoption of advanced AI/ML approaches (e.g. transformer-based models, agentic workflows), leveraging internal and external expertise. 
• Data foundations and standards: Shape enterprise data strategy and principles (FAIR etc.) in partnership with data engineering and platform teams. 
• Global operational leadership: Translate strategy into operational plans and delivery across regions; set the annual operating budget and ensure timely delivery of outcomes within approved levels. Drive continuous improvement, automation and digitalisation with a high-performance culture. 
• Cross-functional and translational partnership: Oversee and guide development of predictive and mechanistic models and ML approaches to identify potential responders and optimize study designs, to generate novel endpoints and biomarkers for clinical studies, and drive disease state understanding. Oversee analytical support for exploratory analyses of interventional clinical trial datasets and analyses of observational/mechanistic/experimental medicine clinical study datasets to derive insights on efficacy, safety, and biomarker correlations related to targets/drug programs in the Early CVRM portfolio. 
• External leadership and partnerships: Represent AZ with academia, consortia, CROs/technology partners, and at conferences; identify and execute strategic partnerships that accelerate scientific and enterprise outcomes. 
• Leadership: Build, mentor, and inspire a multidisciplinary team (bioinformatics, computational biology, data science/ML, data engineering). Performance manage senior managers, plan succession, and coach senior talent 

Essential requirements: 
Education:  

PhD (preferred) or equivalent advanced degree in bioinformatics, computational biology, statistics, computer science, applied mathematics, biomedical engineering, or related field  
 

Leadership:  

+8 years' experience leading bioinformatics/data science organizations in biotech/pharma, including people leadership, org design, hiring, and coaching 
 

Domain depth:  

Demonstrated impact in CVRM-relevant biology (e.g., obesity, diabetes, heart failure, CKD, MASH, atherosclerosis) and/or deep translational/clinical application of omics and real-world data. 
 

Technical breadth:  

Strong background in one or more of the following areas, with working knowledge across others: multi-omics analysis, statistical methods, ML, and modern computational practices (e.g., cloud/HPC, workflow orchestration, versioning, and scalable data processing). 

Experience with or exposure to contemporary transformer-based AI architectur