Outcome Jobs (NOW HIRING)

We'rebuilding a connected, end-to-endEnterprise AIengine - 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'llactivelyleverageexisting 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.

AsSenior Director, Multimodal AI & Outcome PredictionwithinEnterprise AI - AI to Transform Careat 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 treatmentselectionand 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 treatmentselection. 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 clinicallyvalidatedpredictive and diagnostic modelscapable ofidentifyingpatients 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 methodologiesleveragingreal-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.

In this role you will also:

• Contribute to the development of AI for Transform Care team members,providingexpert guidance on precision medicine strategies, companion diagnostics, and AI-embedded clinical decision tools.

• Build and sustain strong internal and external collaborations across Commercial, R&D, key markets, academic leaders, and patient communities to ensure prioritised needs are addressed with scientific excellence.

Requirements

• Advanced degree (Master'sor PhD) in a relevant field such as Biomedical Engineering, Data Science, Computational Biology, Bioinformatics, Digital Health, or Artificial Intelligence.

• + 5 years proven experience leading or contributing to AI-enabled medical or biological projects, such as biomarker discovery, digital pathology, patient stratification, clinical decision support, or diseasemodeling

• Recognizedexpertisein multimodal AI applied to Oncology and BioPharma, withdemonstratedimpact in outcome prediction, computational diagnostics, or precision medicine strategy.

• Deep hands-on mastery of advanced machine learning methodologies including:

• Multimodal representation learning integrating radiology, digital pathology, spatial and bulk omics, molecular diagnostics, digital biomarkers, clinical trials, and real-world data

• Survival modelling, dynamic time-to-event prediction, and competing risk frameworks

• Causal inference methodologies including propensitymodeling, marginal structural models, uplift modelling, and treatment effect heterogeneity analysis

• Construction and validation of synthetic and external control arms using real-world evidence

• Development and validation of prognostic and predictive biomarkers across development phases

• Advanced risk stratification, patient subtyping, clustering, and disease trajectory modelling

• Longitudinal modelling of disease evolution and treatment response

• Strongexpertisein computational imaging, high-dimensional omics integration, and multimodal feature fusion architectures.

• Proven experience defining validation strategies aligned with regulatory-grade evidence standards, including reproducibility frameworks, cross-site generalisability, bias mitigation, robustness testing, and model lifecycle monitoring.

• In-depth understanding of regulatory and compliance frameworks governing AI in healthcare, including medical device pathways, AI governance, transparency requirements, and data privacy regulations.