Senior Director MMAI & Outcome Prediction – AI for Precision Health

AstraZeneca•Boston, MA

11d

About The Position

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. 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, identify comorbidities, and guide treatment selection and 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.

Requirements

Advanced degree (Master’s or 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 disease modeling
Recognized expertise in multimodal AI applied to Oncology and BioPharma, with demonstrated impact 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 propensity modeling, 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
Strong expertise in 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.
Ability to critically dissect external AI architectures, data provenance, validation methodology, and scalability claims.
Extensive experience working with large-scale, heterogeneous healthcare datasets including EHR, claims, imaging repositories, genomic platforms, molecular diagnostic datasets, and global clinical trial databases.
Strong scientific grounding in Oncology biology and clinical development, with the ability to connect modelling outputs to therapeutic mechanisms and development strategy.
Advanced understanding of clinical trial design, enrichment strategies, endpoint optimisation, and evidence package construction.
Solid knowledge of Market Access principles, value-based healthcare frameworks, and payer evidence requirements.
Familiarity with companion diagnostics development and precision medicine strategy integration.
Working knowledge of compliance and legal frameworks relevant to AI-enabled diagnostic and predictive tools.
Deep understanding of healthcare data ecosystems and enterprise platforms, including EMR, CTMS, EDC, imaging systems, molecular data systems, and real-world data infrastructures.
Experience deploying AI models within real-world clinical workflows and complex enterprise environments.
Strong grasp of scalable AI infrastructure, data architecture principles, and model deployment constraints.
Demonstrated track record leading large-scale digital health or AI transformation programs with measurable clinical and economic impact.
Shown ability to shape global strategy and drive adoption across complex, matrixed, multinational organisations.
Experience building and sustaining high-value external partnerships across academia, technology, diagnostics, and data ecosystems.
Ability to translate complex computational concepts into clear strategic implications for senior leadership, regulators, clinicians, and payers.
Entrepreneurial mindset with experience operating in innovation-driven or start-up-like environments.
High level of integrity, scientific rigor, and credibility, with the ability to influence at executive level.
Motivated by delivering scientifically robust digital innovation that materially improves patient outcomes and treatment experience.

Responsibilities

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.
Advance Diagnostic Innovation and Computational Disease Stratification Lead the development of AI-enabled diagnostic frameworks that combine imaging phenotypes, molecular signatures, and clinical data to identify disease 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.
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, identify predictive 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.
Reinforce Foundation Models with Clinical and Real-World Data Partner closely with internal AI research teams to translate advances in foundation models into practical biomedical applications. Design reinforcement strategies that leverage AstraZeneca’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.
Integrate Clinical Trials and Real-World Evidence into Continuous 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.
Establish Enterprise 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.
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.
Develop Strategic External Partnerships in AI and Diagnostics Identify and 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.
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.
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.