Executive Director, AI for Clinical Intelligence and Evidence

Boston, NH - USA

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

TheExecutive Director AI for Clinical Intelligence and Evidenceis a senior enterprise leader responsible for defining how AstraZeneca designsvalidates scales and externalizes AI-driven evidence capabilities across the full lifecycle of its medicines.

This roleestablishesand 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 learningevidenceecosystems.

Defining how these foundation models arevalidated 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.

Enabling 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-enabledevidenceassets including external value creation models aligned with population health and value-based care frameworks.

Theobjectiveis to transition AstraZeneca from episodic evidence generation tocontinuouslylearning AI-enabled evidence infrastructures that support precision medicine real-time value demonstrationand sustainable market access.

SupportallTier 1 Ph3ID andTier 1 COMMIDs with RWE and AI packages for development providing 5pp PTS for clinical relevance and 2 months commercial optimization for launchesby 2030

Support Key Disease Area Strategies with AI enabled RWE packages

Influencesemanticlayers toreflectstrategic visionofAISI &AITC

PioneerAITCAISIEDEClinical Intelligence and Evidenceoperatingmodel

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.

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 designsvalidates industrializes and externalizes AI-driven evidence capabilities across the full product lifecycle.

You willestablishthe roadmap for:

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 developedvalidated governed and scaled across the portfolio

How AI capabilities transition from isolated analyses to repeatable portfolio-wide evidence engines

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:

Identifyresponder 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

Continuouslyvalidatetrial 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

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 evidencemethodologyand itsappropriate application.

4. Regulatory HTA & External Leadership

PositionAstraZeneca 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 evidencemonitoring.

In close collaboration with the AI for Precision Healthcare team 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

Ensuringappropriate governanceand 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 partnerships aligned to therapeutic and asset-levelevidenceneeds.

Establish clear plans for:

Securing harmonized high-quality multimodal datasets

Integrating clinical claims imaging genomic and biomarker data into scalable evidence infrastructures

Governing data use under strong compliance and privacy frameworks

Enabling external deployment of AI-enabled evidence solutions within health system platforms where appropriate

Define how AI-generated evidence capabilities and foundation models may create external value whether through partnerships ecosystem embedding or scalable evidence services aligned with health system and payer priorities.

Leadership

Build and lead a multidisciplinary team combiningexpertisein clinical development epidemiology real-world evidence data science and advanced enterprise capability at the intersection of clinical science AI transformation and lifecycle a culture of scientific rigor responsible AI use cross-functional collaboration and measurable continuous capability development to ensure AstraZenecaremainsat the forefront of AI-driven evidence generation.

Education QualificationsSkillsand Experience

Essential

Advanced degree (PhD MD or MSc) in Statistics Epidemiology Data Science Mathematics orrelated field.

15 years of experience in biopharma with deepexpertiseinclinical development andevidence 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 andRWEapplications.

Demonstrated ability to translate complex outputs into clear decision-ready insights.

Strong knowledge of regulatory and payer expectationsforreal-world validation evidence generation and value demonstration.

Excellent communication executive presence and cross-functional alignment capabilities.

Desirable

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 regulatorsandHTA 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 clinicaland AIexpertise.

Exposure to enterprise data platformsandlarge-scale multimodal data integration initiatives.

The annual base salary for this position in the US ranges from $ - $. However base pay offered may vary depending on multiple individualized factors including market location job-related knowledge skills and experience. In addition our positions offer a short-term incentive bonus opportunity; eligibility toparticipatein our equity-based long-term incentive program (salaried roles) or to receive a retirement contribution (hourly roles). Benefits offered included a qualified retirement program 401(k) plan; paid vacation and holidays; paid leaves; and health benefits including medical prescription drug dental and vision coveragein accordance withthe terms and conditions of the applicable plans.Additionaldetails of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired employee will be in an at-will position and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time including for reasons related to individual performance Company or individual department/team performance and market factors.

Date Posted

06-mar-2026

Closing Date

12-mar-2026

Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and furtherance of that mission we welcome and consider applications from all qualified candidates regardless of their protected characteristics. If you have a disability or special need that requires accommodation please complete the corresponding section in the application form.

Required Experience:

Director