Executive Director, AI for Clinical Intelligence and Evidence

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

The Executive Director, AI for Clinical Intelligence and Evidence is a senior enterprise leader responsible for defining how AstraZeneca designs, validates, scales, and externalizes AI-driven evidence capabilities across the full lifecycle of its medicines. This role establishes and leads a critical capability within the AI to Transform Care (AITC) organization, integrating clinical trial data, real-world data, multimodal biomarker inputs, and advanced analytics into continuously learning evidence ecosystems. Beyond traditional evidence generation, this function is accountable for:

• •Designing and governing disease-specific and multimodal foundation models that integrate clinical, molecular, imaging, and real-world data to support continuously learning evidence ecosystems.
• •Defining how these foundation models are validated, regulated, and made HTA- and payer-acceptable.
• •Translating insights into decision-grade evidence that directly informs development strategy, regulatory interactions, medical planning, access positioning, and lifecycle management.
• •Orchestrating AI-driven analytical and agentic workflows that transform integrated data into continuously generated, decision-ready evidence embedded within development, regulatory, and commercial processes.
• •Driving scalable deployment of AI-enabled evidence capabilities across health systems through strategic partnerships (e.g., EMR-embedded solutions, real-world care networks, and federated data ecosystems).
• •Establishing closed-loop, continuously learning evidence systems that feed real-world outcomes back into development, regulatory, medical, and commercial decision-making.
• •Defining commercialization pathways for AI-enabled evidence assets, including external value creation models aligned with population health and value-based care frameworks.
• •The objective is to transition AstraZeneca from episodic evidence generation to continuously learning, AI-enabled evidence infrastructures that support precision medicine, real-time value demonstration, and sustainable market access.
• •Support all Tier 1 Ph3 and Tier 1 COMMID's with RWE and AI packages for development providing +5pp PTS for clinical relevance and +2 months commercial optimization for launches by 2030
• •Support Key Disease Area Strategies with AI-enabled RWE packages (e.g., RWE analysis to predict risk of disease progression based on multimodal features)
• •Influence semantic layers to reflect strategic vision of AISI & AITC
• •Pioneer AITC, AISI, EDE, Clinical Intelligence and Evidence operating model

Overall, this function positions AI-generated evidence and foundation models not as analytical tools, but as strategic enterprise assets, driving differentiation in development, accelerating access, strengthening payer confidence, and enabling scalable transformation of care.

Typical Accountabilities

1. Strategy, Portfolio & Foundation Model Ownership

As the enterprise lead for AI-enabled evidence across priority therapeutic areas, you will define and execute a multi-year strategy for how AstraZeneca designs, validates, industrializes, and externalizes AI-driven evidence capabilities across the full product lifecycle. You will establish the roadmap for:

• •How AI capabilities transition from isolated analyses to repeatable, portfolio-wide evidence engines
• •How AI integrates clinical trial data, real-world data, biomarker information, and multimodal inputs into continuously learning evidence frameworks
• •How disease-specific and multimodal foundation models are developed, validated, governed, and scaled across the portfolio
• •How AI-generated evidence assets can be externalized and positioned within health system ecosystems, aligned with population health and value-based care models

For multimodal outcome prediction and disease modeling, you will work in close partnership with the AI Precision for Health team, providing scientific validation leadership, methodological oversight, and evidence translation strategy while ensuring models meet regulatory, medical, and payer standards.

You will align investments toward high-impact assets where AI-generated evidence and foundation models can materially strengthen regulatory positioning, competitive differentiation, and long-term asset value.

2. Integration of Clinical, Real-World & Multimodal Evidence

Establish enterprise standards and scalable operating models to transform multimodal clinical and real-world data into decision-grade evidence. Define and industrialize AI-enabled methodologies to:

• •Identify responder subgroups and treatment heterogeneity in clinical trials
• •Model disease progression and predict treatment response using multimodal datasets
• •Generate synthetic or external control arms when appropriate
• •Continuously validate trial findings through real-world monitoring
• •Enable real-time outcome tracking aligned with value demonstration

Ensure all AI-generated evidence is transparent, explainable, reproducible, and methodologically robust.

Critically, define validation frameworks that make AI-enabled evidence and foundation models acceptable to regulators, HTA bodies, and payers — including standards for explainability, performance benchmarking, bias monitoring, and ongoing model recalibration.

3. Governance, Scientific Rigor & Decision Integration

Embed AI-enabled evidence outputs into formal governance forums across development, medical planning, access strategy, and lifecycle management. Define how AI-generated insights are:

• •Scientifically validated
• •Explainable and interpreted in context
• •Translated into development and commercial decisions

Establish enterprise standards for responsible AI use in evidence generation, including model validation, monitoring, transparency, auditability, and human accountability.

Serve as the recognized enterprise authority on AI-driven evidence methodology and its appropriate application.

4. Regulatory, HTA & External Leadership

Position AstraZeneca as a global leader in the responsible use of AI-enabled and real-world evidence.

Engage with regulators and scientific bodies to advance credible and transparent application of AI-driven methodologies across the product lifecycle, including post-approval validation and continuous evidence monitoring.

In close collaboration with the AI for Precision Health team within AI to Transform Care (AITC), support the development of validation frameworks that enable AI-generated evidence and multimodal foundation models to be acceptable to HTA agencies and payer stakeholders. This includes:

• •Defining methodological standards for robustness, reproducibility, and explainability
• •Supporting performance benchmarking and bias monitoring frameworks
• •Ensuring appropriate governance and model recalibration standards
• •Translating AI-generated outputs into formats aligned with value demonstration and access discussions

Contribute to shaping industry standards for the validation, governance, and responsible deployment of AI-driven evidence approaches, while ensuring alignment with enterprise access and precision healthcare strategies.

5. AI-Enabled Data, Ecosystem & Commercialization Strategy

Define strategic priorities for AI-ready data partner