Executive Director, AI for Clinical Intelligence and Evidence

AstraZeneca • Gaithersburg, MD

1d • $258,158 - $387,236 • Hybrid

About The Position

The Executive Director, AI for Clinical Intelligence and Evidence is a senior enterprise leader in AstraZeneca’s AI to Transform Care organization. The role defines how the company designs, validates, scales, and externalizes AI-driven evidence capabilities across the lifecycle of its medicines. It integrates clinical trial data, real-world data, multimodal biomarker inputs, and advanced analytics into continuously learning evidence ecosystems, shifting the organization from episodic evidence generation to AI-enabled infrastructure for precision medicine, real-time value demonstration, and sustainable market access.

The role owns the strategy and roadmap for disease-specific and multimodal foundation models, including their development, governance, validation, regulatory readiness, and acceptance by health technology assessment bodies and payers. It translates AI-generated insights into decision-grade evidence for development strategy, regulatory interactions, medical planning, access positioning, and lifecycle management. It also leads the integration of AI-driven analytical and agentic workflows into business processes, promotes scalable deployment through health-system partnerships and data ecosystems, and establishes approaches for externalizing and commercializing AI-enabled evidence assets.

The Executive Director partners with the AI Precision for Health team on multimodal outcome prediction and disease modeling, providing scientific validation leadership, methodological oversight, and evidence translation strategy. The role also supports Tier 1 Phase 3 and Tier 1 commercial launches with real-world evidence and AI packages, supports disease-area strategies, influences semantic layers to reflect the strategic vision of AISI and AITC, and helps establish the AITC, AISI, EDE, and Clinical Intelligence and Evidence operating model.

The position builds and leads a multidisciplinary team spanning clinical development, epidemiology, real-world evidence, data science, and advanced analytics. It promotes scientific rigor, responsible AI, collaboration, and measurable impact, and represents AstraZeneca in relevant external scientific, regulatory, and industry discussions. The role is expected to work from an office an average of at least three days per week, with flexibility respected.

Requirements

• Advanced degree (PhD, MD, or MSc) in Statistics, Epidemiology, Data Science, Mathematics, or a related field.
• At least 15 years of biopharma experience, with deep expertise in clinical development and evidence strategy across the product lifecycle.
• Demonstrated leadership of global, matrixed teams with enterprise-level influence.
• Proven experience applying AI and advanced analytics to clinical and real-world datasets.
• Experience influencing evidence planning and portfolio-level strategy.
• Strong understanding of clinical trial design, progression modelling, and real-world evidence applications.
• Ability to translate complex outputs into clear, decision-ready insights.
• Strong knowledge of regulatory and payer expectations for real-world validation, evidence generation, and value demonstration.
• Excellent communication, executive presence, and cross-functional alignment capabilities.

Nice To Haves

• Experience in oncology, precision medicine, or other biomarker-driven therapeutic areas.
• Experience deploying AI-driven evidence capabilities across multiple assets or portfolios.
• Experience with synthetic control arm methodologies and comparative modelling.
• Experience engaging directly with regulators and HTA bodies on advanced evidence methodologies.
• Experience overseeing AI/ML model lifecycle governance, validation, and monitoring in production environments.
• Experience building and scaling multidisciplinary teams combining clinical and AI expertise.
• Exposure to enterprise data platforms and large-scale multimodal data integration initiatives.

Responsibilities

• Define and execute a multi-year enterprise strategy for designing, validating, industrializing, and externalizing AI-driven evidence capabilities across the medicine lifecycle.
• Establish a roadmap to move AI capabilities from isolated analyses to repeatable, portfolio-wide evidence engines.
• Lead the development, validation, governance, and scaling of disease-specific and multimodal foundation models across the portfolio.
• Align investments toward assets where AI-generated evidence can strengthen regulatory positioning, competitive differentiation, and long-term asset value.
• Partner with the AI Precision for Health team on multimodal outcome prediction and disease modeling, providing scientific validation leadership, methodological oversight, and evidence translation strategy.
• Establish enterprise standards and scalable operating models for transforming multimodal clinical and real-world data into decision-grade evidence.
• Industrialize AI-enabled methods to identify responder subgroups and treatment heterogeneity, model disease progression and treatment response, generate synthetic or external control arms when appropriate, validate trial findings through real-world monitoring, and enable real-time outcome tracking.
• Ensure AI-generated evidence is transparent, explainable, reproducible, and methodologically robust.
• Define validation frameworks and standards that support regulatory, HTA, and payer acceptance, including explainability, performance benchmarking, bias monitoring, and ongoing model recalibration.
• Embed AI-enabled evidence outputs into governance forums for development, medical planning, access strategy, and lifecycle management.
• Establish responsible AI standards for evidence generation, including model validation, monitoring, transparency, auditability, and human accountability.
• Serve as the enterprise authority on AI-driven evidence methodology and its appropriate application.
• Engage with regulators and scientific bodies to advance credible and transparent AI-driven methodologies across the product lifecycle, including post-approval validation and continuous evidence monitoring.
• Help shape industry standards for validation, governance, and responsible deployment of AI-driven evidence approaches.
• Define strategic priorities and plans for AI-ready data partnerships, harmonized multimodal datasets, scalable evidence infrastructure, compliant data use, and external deployment of evidence solutions where appropriate.
• Define pathways for external value creation from AI-generated evidence capabilities and foundation models, including partnerships, ecosystem embedding, and scalable evidence services.
• Build and lead a multidisciplinary team across clinical development, epidemiology, real-world evidence, data science, and advanced analytics; develop enterprise capability and foster scientific rigor, responsible AI, collaboration, and measurable impact.
• Support Tier 1 Phase 3 and Tier 1 commercial launches with RWE and AI packages, with stated targets of +5 percentage points for clinical relevance and two months of commercial optimization for launches by 2030.
• Support key disease-area strategies with AI-enabled RWE packages, such as multimodal analysis to predict disease-progression risk.
• Influence semantic layers to reflect the strategic vision of AISI and AITC, and pioneer the AITC, AISI, EDE, and Clinical Intelligence and Evidence operating model.

Benefits

• Short-term incentive bonus opportunity.
• Eligibility to participate in an equity-based long-term incentive program.
• Qualified retirement program, including a 401(k) plan.
• Paid vacation and holidays.
• Paid leaves.
• Medical, prescription drug, dental, and vision coverage, subject to applicable plan terms and conditions.

What This Job Offers

• Job Type: Full-time
• Career Level: Executive