Senior Director, AI for Clinical Intelligence & Evidence Breast & Gynaecology

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, AI for Clinical Intelligence & Evidence, Breast & Gynaecology, you will lead a multidisciplinary team responsible for generating high-quality clinical intelligence and evidence across the Breast & Gynaecology portfolio. You will combine deep expertise in Real-World Evidence and epidemiology with strong people leadership, strategic prioritisation and an understanding of AI and machine learning to ensure the team focuses on the questions where it can deliver the greatest value. You will work closely with drug development teams to translate complex clinical and portfolio questions into research objectives that can be addressed through real-world data and advanced methods. You will help determine where RWE and AI can make a meaningful difference, guide the scientific approach, and ensure insights are translated into evidence that supports drug development and better outcomes for patients. The position sits within AI for Clinical Intelligence & Evidence, part of AI to Transform Care, whose mission is to apply AI across the care pathway to identify patients faster, guide better treatments and improve outcomes at scale. The function integrates clinical, trial and real-world data to support regulatory, HTA and payer-grade evidence, establish real-time and agentic RWE systems, and generate clinical intelligence for decision-making. How would you combine AI, real-world data, clinical evidence and scientific judgement to anticipate critical portfolio questions and generate the intelligence needed to answer them? Accountabilities: Breast & Gynaecology CIE Strategy: Define and evolve the Clinical Intelligence & Evidence strategy for Breast & Gynaecology, aligning the team's scientific priorities and capabilities with the needs of the Oncology portfolio. Real-World Evidence Leadership: Provide scientific leadership in epidemiology and Real-World Evidence, advising on appropriate study designs, analytical methods and the use of routinely collected healthcare data to address important clinical and development questions. Portfolio Partnership: Work closely with drug development teams to understand their priorities, identify where Real-World Evidence, AI and advanced methods can add value, and translate clinical and portfolio questions into clear research objectives. Clinical Development: Apply Real-World Evidence to support clinical development questions AI and Machine Learning: Shape the appropriate use of machine learning and AI within Real-World Evidence, identifying opportunities where these methods can strengthen evidence generation while distinguishing meaningful scientific applications from technological hype. Advanced Methods: Support the application of approaches including predictive modelling, patient subtyping, disease progression modelling, temporal modelling and the extraction of insights from clinical text and other real-world data. People Leadership: Lead and develop a multidisciplinary team, setting clear objectives, providing regular feedback and coaching, supporting career development and building an inclusive, collaborative and high-performing team environment. Strategic Prioritisation: Prioritise the team's work according to its potential impact on the Oncology portfolio and patients, balancing immediate drug development needs with longer-term scientific and capability priorities. Cross-Functional Leadership: Build strong relationships across drug development, clinical, scientific, data and AI teams, bringing together the expertise required to address complex evidence questions. Influence across Global Product Teams, matrix teams and external partners to keep evidence outputs both technically rigorous and strategically relevant. Cross-CIE Collaboration: Partner with other Clinical Intelligence & Evidence teams to share expertise, develop common capabilities and scale valuable AI approaches across therapeutic-area portfolios. External Collaboration: Work with external organisations where appropriate to access Real-World Data, expertise and capabilities that strengthen evidence generation, including healthcare organisations, data providers and research collaborators. Scientific Impact: Contribute to the scientific advancement of Real-World Evidence and AI-enabled clinical intelligence, ensuring work is rigorous, clinically meaningful and focused on improving drug development and patient outcomes. Change Leadership: Help shape the wider Clinical Intelligence & Evidence operating model, promoting collaboration across disease-area teams and accelerating the transition from isolated analyses to scalable, enterprise-level capabilities. Education, Qualifications, Skills and Experience Essential Advanced degree (PhD, MPH, MD, or MSc) in Statistics, Epidemiology, Public Health, Data Science, Mathematics, or related field. 10+ years of experience in life sciences or healthcare with deep expertise in epidemiology and Real-World Evidence generation, including the use and application of RWD to support clinical decision-making. Strong experience with health analytics and routinely collected healthcare data, with the ability to assess different data sources and approaches for fitness for purpose. Experience working with multidisciplinary teams to translate clinical and development questions into research objectives that can be addressed using RWD and appropriate epidemiological and statistical methods. Experience in quantitative science, including health outcomes modelling, health economics, epidemiology or related disciplines, and familiarity with statistical and scripting languages. Understanding of Oncology drug development, particularly Phase 3 and beyond, including the role of RWE in clinical trials, patient recruitment, evidence generation and external or synthetic control approaches. Experience with AI/ML methods, or a strong understanding of their principles, strengths, limitations and appropriate application to Real-World Data. Experience with regulatory submissions or HTA dossiers leveraging RWE. Proven experience line managing direct reports, developing talent and building effective, multidisciplinary teams. Demonstrated ability to translate complex outputs into clear, decision-ready insights. Demonstrated ability to prioritise work strategically, manage competing demands and adapt team focus and resources as Oncology strategy and portfolio needs evolve. Ability to work effectively across clinical, scientific, quantitative and technology disciplines within a global, matrixed organisation. Strong communication and influencing skills, including the ability to communicate complex scientific topics, evidence and trade-offs clearly to senior stakeholders. Desirable Experience within Oncology, particularly Breast and/or Gynaecological cancers Hands-on experience in data science, machine learning and predictive modelling. Familiarity with advanced approaches including Bayesian inference, temporal modelling, multimodal modelling, foundation models, and model interpretability and evaluation. Experience working with clinical text, electronic health records, imaging or molecular data, including genomics in clinical care or translational medicine. Knowledge of clinical data standards, medical terminologies and healthcare ontologies. Experience in late-stage drug development and access, including Phase 3 clinical trial design, biomarkers, safety, endpoints, and regulatory, HTA or payer interactions. Experience developing or applying external or synthetic control arms and other approaches that integrate RWD with clinical trials. Experience working in a patient-care or similar setting, bringing a medical or clinical perspective to RWE generation. Experience establishing or managing collaborations with academic institutions, healthcare organisations, technology partners, data providers or industry consortia. Up-to-date knowledge of developments in AI and machine learning, with the scientific judgement to distinguish meaningful applications from technological hype. Are you ready to shape how real-world evidence, data and AI are used to answer some of the most important questions in Breast & Gynaecology Oncology? Why AstraZeneca: At AstraZeneca, AI, clinical science and real-world evidence come together to address some of the most important questions in medicine. You will work alongside clinical leaders, data scientists, epidemiologists, statisticians and technology experts to transform complex data into evidence that improves development decisions and, ultimately, patient outcomes. You will have the opportunity to shape a growing enterprise capability rather than simply deliver individual analyses. The environment combines rich clinical and real-world data, ambitious scientific programmes, advanced AI capabilities and the scale required to move successful approaches across assets and therapeutic areas. This is a role for a scientific leader who wants to build for the long term: strengthening evidence, developing people, creating reusable capabilities and helping establish a more proactive, continuously learning approach to clinical intelligence. Office Working Requirements: When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. The annual base pay for this position ranges from $ 203.213,60 - 304.820,40 Annual USD. 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 to participate in our equity-based long-term incentive program. Benefits offered include a qualified retirement program [401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans. #EAI Date Posted 09-nov-2026 Closing Date 27-nov-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In 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.