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

AstraZeneca•Gaithersburg, MD

18h•Hybrid

About The Position

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 bring deep expertise in epidemiology and Real-World Evidence (RWE), people leadership, strategic prioritisation, and an understanding of AI and machine learning to focus the team on questions with the greatest potential value. You will partner with drug development teams to turn complex clinical and portfolio questions into research objectives addressable through real-world data and advanced methods. You will help identify where RWE and AI can contribute meaningfully, guide scientific approaches, and ensure insights become evidence that supports drug development and better patient outcomes. The role sits within AI for Clinical Intelligence & Evidence, part of AI to Transform Care, which applies 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. The role also contributes to the wider Clinical Intelligence & Evidence operating model by promoting collaboration across disease-area teams and moving from isolated analyses to scalable enterprise capabilities. The position requires an advanced degree and at least 10 years of relevant life sciences or healthcare experience. The role is based on an average minimum of three office days per week, with individual flexibility accommodated. The annual base pay range is $203,213.60–$304,820.40, with short-term incentive bonus opportunity and eligibility for an equity-based long-term incentive program.

Requirements

Advanced degree (PhD, MPH, MD, or MSc) in Statistics, Epidemiology, Public Health, Data Science, Mathematics, or a related field.
At least 10 years of experience in life sciences or healthcare, with deep expertise in epidemiology and Real-World Evidence generation, including applying real-world data to clinical decision-making.
Strong experience with health analytics and routinely collected healthcare data, including assessing data sources and approaches for fitness for purpose.
Experience translating clinical and development questions into research objectives addressable using real-world data and appropriate epidemiological and statistical methods in multidisciplinary teams.
Experience in quantitative science, such as health outcomes modelling, health economics, or epidemiology, and familiarity with statistical and scripting languages.
Understanding of Oncology drug development, particularly Phase 3 and beyond, including RWE in clinical trials, patient recruitment, evidence generation, and external or synthetic control approaches.
Experience with AI/ML methods, or 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.
Ability to translate complex outputs into clear, decision-ready insights.
Ability to prioritise strategically, manage competing demands, and adapt team focus and resources as Oncology strategy and portfolio needs evolve.
Ability to work across clinical, scientific, quantitative, and technology disciplines in a global, matrixed organisation.
Strong communication and influencing skills, including communicating complex scientific topics, evidence, and trade-offs clearly to senior stakeholders.

Nice To Haves

Experience within Oncology, particularly Breast and/or Gynaecological cancers.
Hands-on experience in data science, machine learning, and predictive modelling.
Familiarity with Bayesian inference, temporal modelling, multimodal modelling, foundation models, and model interpretability and evaluation.
Experience 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 trial design, biomarkers, safety, endpoints, and regulatory, HTA, or payer interactions.
Experience developing or applying external or synthetic control arms and other approaches integrating real-world data with clinical trials.
Experience 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 AI and machine learning developments, with scientific judgement to distinguish meaningful applications from technological hype.

Responsibilities

Define and evolve the Clinical Intelligence & Evidence strategy for Breast & Gynaecology, aligning scientific priorities and capabilities with Oncology portfolio needs.
Provide scientific leadership in epidemiology and Real-World Evidence, advising on study designs, analytical methods, and the use of routinely collected healthcare data.
Partner with drug development teams to identify where RWE, AI, and advanced methods can add value and translate clinical and portfolio questions into research objectives.
Apply Real-World Evidence to support clinical development questions.
Shape the appropriate use of machine learning and AI in RWE, identifying scientifically useful applications and distinguishing them from technological hype.
Support advanced methods including predictive modelling, patient subtyping, disease progression modelling, temporal modelling, and extracting insights from clinical text and other real-world data.
Lead and develop a multidisciplinary team by setting objectives, providing feedback and coaching, supporting career development, and fostering an inclusive, collaborative, high-performing environment.
Prioritise team work according to potential impact on the Oncology portfolio and patients, balancing immediate development needs with longer-term scientific and capability priorities.
Build relationships across drug development, clinical, scientific, data, and AI teams; influence Global Product Teams, matrix teams, and external partners to ensure evidence is rigorous and strategically relevant.
Partner with other Clinical Intelligence & Evidence teams to share expertise, develop common capabilities, and scale valuable AI approaches across therapeutic-area portfolios.
Work with external organisations, including healthcare organisations, data providers, and research collaborators, to access data, expertise, and capabilities that strengthen evidence generation.
Advance Real-World Evidence and AI-enabled clinical intelligence through rigorous, clinically meaningful work focused on drug development and patient outcomes.
Help shape the wider Clinical Intelligence & Evidence operating model, promoting cross-disease-area collaboration and scalable enterprise capabilities.