Associate Director, Clinical Information Science & AI

At AstraZeneca, we are committed to revolutionising cancer care. Our oncology pipeline spans lung, breast, gastrointestinal, genitourinary, and haematological cancers, and we follow the science to deliver life-changing treatments that increase the potential to save lives worldwide. 

ABOUT THE ROLE 

This is a leadership role within the Oncology Information Practice (OIP) team, part of AstraZeneca's Oncology Biometrics organisation. You will combine deep expertise in clinical and scientific information management with applied data science, machine learning, and AI to design, deliver, and continuously improve information systems, data management tools, and intelligent AI-powered applications that support drug development decisions across AstraZeneca's oncology portfolio. 

Operating at the intersection of information science and cutting-edge technology, you will drive the integration of AI and agentic AI into existing tools and data management systems, collaborating with data scientists, engineers, clinical teams, statisticians, and IT professionals to turn complex clinical and scientific data into actionable insights. 

You will act as Product Owner for tools and platforms developed within the team, mentor junior colleagues and interns, and foster a culture of continuous learning and innovation. 

KEY RESPONSIBILITIES 

Data Management & Systems 

• Lead the design and delivery of a cross-project clinical data management system, encompassing data models, ingestion standards, quality controls, metadata cataloguing, lineage, and secure access patterns to enable data interrogation and reuse across studies. 

• Define and own APIs and services that expose curated datasets and analytical capabilities to internal tools and partner teams; ensure reliability via clear SLOs, monitoring, and incident response practices. 

• Architect and deliver integrations between the data management system and upstream/downstream platforms (e.g., EDC, CTMS, eTMF, Statistical Computing Environment) to enable seamless, governed data and code reuse. 

• Translate decision needs into intuitive, data-rich tools (e.g., exploratory analytics, cohort selection, benchmark views); lead usability testing and drive change management, training, and adoption. 

• Establish data stewardship roles, data contracts, and approval workflows; oversee sensitive data handling, periodic access reviews, and validation for audit-readiness in GxP-relevant contexts. 

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Data Science, AI & Agentic AI 

• Design, develop, and deploy data science and AI solutions — including machine learning models, NLP pipelines, and agentic AI components — to enhance existing tools and workflows. 

• Explore and implement novel methods across optimisation, deep learning, generative AI, large language models (LLMs), and multi-agent frameworks. 

• Build and maintain scalable data and analysis pipelines that deliver reusable clinical and scientific insights. 

• Apply NLP techniques (NER, classification, search, language generation) using state-of-the-art models and frameworks (e.g., Hugging Face, BERT, PyTorch, spaCy, LangChain). 

• Develop intelligent applications leveraging knowledge graphs, embeddings, ontologies, and machine learning to capture biological, medical, and clinical knowledge. 

• Translate unstructured business challenges into data and model-driven solutions, applying both foundational and novel methodologies. 

• Ensure all data science work meets robust quality standards, aligned with governance frameworks, AI ethics principles, and regulatory requirements. 

• Communicate results clearly to technical and non-technical stakeholders, including associated uncertainties and limitations.

• Stay current with trends in AI, agentic systems, and data science, championing their adoption across the team. 

Leadership & Collaboration 

• Provide strategic and technical leadership on AI and information science initiatives, influencing direction across Oncology Biometrics. 

• Mentor and support colleagues, promoting best practice in AI, NLP, and information science. 

• Lead and contribute to cross-functional, multidisciplinary projects working with data scientists, engineers, clinicians, statisticians, and IT professionals. 

• Disseminate findings and innovations at conferences and in peer-reviewed journals. 

• Operate with significant autonomy, managing priorities, timelines, and stakeholder expectations effectively. 

TECHNICAL REQUIREMENTS 

• Programming & Analysis: Proficiency in Python, R, and SQL for data manipulation, analysis, and modelling. 

• Data Visualisation: Experience with Tableau, Power BI, or equivalent platforms to build intuitive stakeholder-facing dashboards. 

• ETL & Databases: Sound understanding of ETL processes and database systems for efficient data ingestion, transformation, and retrieval. 

• Microsoft 365 Platform: SharePoint, PowerApps, and Power Automate for data organisation, custom solutions, and workflow automation. 

• Web Development: End-to-end development of secure, scalable web applications — back end (Python/Node.js), front end (React/TypeScript), and APIs (REST/GraphQL) — with a focus on testing, accessibility, and performance. 

• Clinical/R&D Integrations: Experience integrating with systems such as EDC, CTMS, and Statistical Computing Environments. 

• Data Governance & Quality: Hands-on experience with data contracts, quality monitoring, metadata catalogues, data lineage, and secure access controls with audit trails. 

• Cloud & DevSecOps: Experience on a major cloud platform (Azure preferred), including containers, infrastructure as code, CI/CD pipelines, and observability practices (feature flags, blue-green/canary releases). 

• Agile Delivery: Demonstrated experience working within Agile methodologies and delivering tools to production with long-term maintenance in mind. 

• Knowledge Representation: Working knowledge of ontologies, taxonomies, and knowledge graphs. 

• Innovation: Proven track record of leading the design and delivery of innovative technical solutions. 

ESSENTIAL REQUIREMENTS 

Education & Experience 

• Bachelor's or Master's degree in Computer Science, Data Science, Information Science, Mathematics, Statistics, Bioinformatics, or a related quantitative or scientific discipline. 

• Significant industry experience in clinical or scientific information management, data science, or a combination of both. 

• Demonstrated experience at a senior or associate director level (or equivalent), with a track record of leading technical and information projects. 

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DESIRABLE REQUIREMENTS 

• PhD in a relevant quantitative or scientific discipline. 

• Experience with knowledge graphs, ontologies, reasoning, and knowledge representation. 

• Background in oncology, immuno-oncology, or related life sciences disciplines. 

• Familiarity with high-performance computing or large-scale cloud-based data processing. 

• Demonstrated experience applying data science within the pharmaceutical or life sciences domain. 

• Experience contributing to or leading AI integration into existing data management or information systems. 

• Track record of scientific publication or conference presentations in AI, NLP, or information science. 

• Business analysis and consultancy skills, including translating strategic information needs into technology solutions.