As Senior Director, Multimodal AI & Outcome Prediction within Enterprise AI – AI to Transform Care at AstraZeneca, you will lead the scientific translation of multimodal artificial intelligence and foundation model advances into clinically actionable capabilities across Oncology and BioPharma. Working in close collaboration with Enterprise AI, R&D teams, and AI for Science Innovation (AISI), you will drive the development, reinforcement, and validation of multimodal predictive and diagnostic systems integrating radiology, digital pathology, multi-omics (genomics, transcriptomics, proteomics), molecular diagnostics, clinical trial datasets, real-world electronic health records and claims, and longitudinal patient signals including digital biomarkers. Your work will enable the discovery and validation of AI-derived multimodal biomarkers and computational disease taxonomies that improve early diagnosis, refine disease stratification, support companion and AI-enabled diagnostic strategies,identifycomorbidities, and guide treatment selectionand responder identification. By applying advanced representation learning, outcome modelling, and survival analytics, you will translate multimodal intelligence into clinical development impact through trial enrichment, patient identification, endpoint optimisation, and deeper reanalysis of clinical trial data. In parallel, you will help reinforce foundation models using AstraZeneca’s multimodal trial and real-world datasets, creating continuous learning systems that connect discovery, development, diagnostics, and real-world outcomes across the product lifecycle. The role will also establish enterprise scientific standards for multimodal AI, including validation frameworks, cross-site robustness, regulatory-grade evidence generation, and performance monitoring, ensuring that AI-enabled diagnostic and predictive models can be trusted, scaled, and deployed to improve patient outcomes and accelerate precision medicine across the portfolio. Key Mission 1.Scientific Leadership in Multimodal AI and Computational Diagnostics Act as the enterprise scientific authority for multimodal AI applied to Oncology and BioPharma. Define and drive the scientific agenda for predictive modelling and computational diagnostics by developing advanced multimodal methodologies integrating imaging, molecular diagnostics, omics data, clinical trial datasets, digital biomarkers, and real-world evidence. Champion methodological excellence in multimodal representation learning, computational imaging, omics integration, disease trajectory modelling, and survival prediction. Ensure the scientific rigor, reproducibility, and robustness of AI models used to derive predictive biomarkers, diagnostic intelligence, and patient stratification strategies. 2. Advance Diagnostic Innovation and Computational Disease Stratification Lead the development of AI-enabled diagnostic frameworks that combine imaging phenotypes, molecular signatures, and clinical data toidentifydisease states earlier and refine biological disease taxonomy. Drive the discovery and validation of multimodal biomarkers that support early diagnosis, disease subtype classification, and treatment selection. Contribute to the development of companion diagnostics and AI-enabled diagnostic strategies aligned with precision medicine and regulatory requirements, enabling improved patient identification and clinical decision support. 3. Transform Clinical Development Through Predictive Intelligence Apply multimodal AI methodologies to transform clinical development strategies by improving patient identification, trial enrichment, responder prediction, and endpoint optimisation. Lead advanced reanalysis of clinical trial datasets to uncover responder subgroups,identifypredictive and prognostic biomarkers, and refine patient selection strategies. Use advanced modelling approaches such as causal inference, treatment effect estimation, and dynamic outcome prediction to strengthen development decisions and maximise asset differentiation across the portfolio. 4. Reinforce Foundation ModelswithClinical and Real-World Data Partner closely with internal AI research teams to translate advances in foundation models into practical biomedical applications. Design reinforcement strategies thatleverageAstraZeneca’s clinical trial datasets, real-world healthcare data, and multimodal biological signals to improve model generalisability and predictive power. Develop reusable multimodal representations that capture disease biology across datasets and therapeutic areas, enabling scalable predictive modelling capabilities across the organisation. 5. Integrate Clinical Trials and Real-World EvidenceintoContinuous Learning Systems Establish predictive modelling frameworks that integrate clinical trial data with real-world evidence to extend insights beyond controlled trial environments. Develop continuous learning systems capable of incorporating longitudinal patient outcomes from electronic health records, claims data, and diagnostic platforms. Enable post-launch monitoring of treatment outcomes and reinforcement of predictive models through real-world evidence, creating feedback loops that strengthen both development and care pathway strategies. 6.EstablishEnterprise Standards for Multimodal AI Validation and Governance Define and implement enterprise-wide scientific standards for the validation, deployment, and lifecycle management of multimodal AI models. Establish rigorous frameworks for reproducibility, cross-site generalisability, bias mitigation, model explainability, and regulatory-grade evidence generation. Ensure that predictive and diagnostic models meet the scientific, regulatory, and operational requirements necessary for deployment in clinical research and healthcare environments. 7. Bridge R&D, Diagnostics, and Transform Care Initiatives Act as a strategic bridge between R&D, diagnostics, and care transformation initiatives by ensuring that multimodal predictive models developed during clinical development translate into scalable tools used in real-world clinical practice. Enable the integration of molecular diagnostics, imaging capabilities, and digital biomarkers into unified predictive frameworks that support patient identification, treatment optimisation, and outcome prediction across the care continuum. 8. Develop Strategic External Partnerships in AI and Diagnostics Identifyand engage leading academic, AI, diagnostics, and real-world data partners to accelerate innovation in multimodal predictive modelling and computational diagnostics. Evaluate external technologies, datasets, and algorithms to ensure methodological robustness, scalability, and regulatory readiness. Establish collaborative development programs that advance scientific capabilities while protecting intellectual property and ensuring enterprise integration. 9. Drive Cross-Functional Collaboration and Strategic Alignment Lead multidisciplinary collaboration across research, translational medicine, data science, diagnostics, medical affairs, commercial, and market access teams. Align predictive modelling initiatives with therapeutic area strategies, development priorities, regulatory pathways, and payer evidence requirements. Translate complex methodological insights into clear clinical, regulatory, and strategic implications for senior leadership and global stakeholders. 10. Elevate Organisational Capability in AI-Driven Precision Medicine Build and institutionalise advanced capabilities in multimodal AI, computational diagnostics, predictive biomarker development, and outcome modelling. Mentor scientific and digital teams to ensure methodological excellence, transparency, and clinical relevance. Contribute to positioning AstraZeneca as a global leader in AI-enabled precision medicine and computational diagnostics. Initial Focus and Expected Outcomes • Launch flagship multimodal AI programsintegrating imaging, molecular diagnostics, clinical trial datasets, and real-world evidence to enable earlier disease detection, refined disease stratification, and superior outcome prediction across priority Oncology and BioPharma indications. • Deliver clinicallyvalidated predictive and diagnostic modelscapable of identifying patients earlier in the disease trajectory, improving risk stratification, guiding treatment selection, and forecasting longitudinal outcomes, with clear pathways toward regulatory-grade validation and real-world deployment. • Advance multimodal biomarker and computational diagnostic strategiesthat integrate radiology, digital pathology, omics data, and digital biomarkers to refine disease taxonomy, identifybiologically meaningful subtypes, and support precision medicine approaches including companion diagnostics and AI-enabled diagnostic tools. • Establish robust predictive modelling frameworksfor survival analysis, disease trajectory modelling, treatment effect estimation, and responder identification, enabling improved trial enrichment strategies, stronger endpoint optimisation, and enhanced asset differentiation across development programs. • Build scalable synthetic and external control arm methodologiesleveraging real-world evidence and multimodal datasets to accelerate clinical development, strengthen regulatory evidence packages, and support health technology assessment and payer value demonstration. • Create continuous learning systemsthat integrate clinical trial data, diagnostic platforms, and real-world patient outcomes, enabling ongoing reinforcement of predictive models and sustained improvement of diagnostic and outcome prediction capabilities throughout the product lifecycle. • Define enterprise standards for multimodal AI validation and deployment, including reproducibility frameworks, cross-site generalisability testing, regulatory-grade evidence generation, bias mitigation strategies, and model performance monitoring in real-world clinical environments. • Demonstrate measurable clinical and economic impactby delivering AI-enabled predictive and diagnostic capabilities that improve patient identification, optimise treatment strategies, accelerate development timelines, and support value-based healthcare across multiple therapeutic areas and geographies.