Senior Director, Lung and HNSCC Clinical Intelligence & RWE Strategy

This role can be based at AstraZeneca hubs in Boston, US or Gaithersburg, US; 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. Introduction to role Shape the evidence that determines which medicines reach patients—and how quickly As Senior Director, Lung and HNSCC Clinical Intelligence & RWE Strategy, you will lead the Lung and HNSCC Therapeutic Area, setting the strategic direction for how clinical intelligence and real-world evidence are generated, interpreted, and applied across the portfolio. You will translate portfolio priorities, senior stakeholder needs, and emerging scientific opportunities into a focused evidence agenda. You will identify where the team can create the greatest scientific, clinical, and business value; originate the questions that require deeper investigation; design the appropriate evidence approach; and bring together the expertise required to generate decision-ready insight. Your work will inform clinical development, Phase II/III transition decisions, trial design, patient stratification, biomarker strategy, regulatory confidence, medical strategy, market access, and portfolio investment. You will also help envision how agentic workflows, emerging AI capabilities, and connected evidence systems can improve the way clinical intelligence and RWE are generated, synthesized, and applied. You will identify where these capabilities can accelerate scientific insight, connect fragmented information, and enable more proactive decision-making, while ensuring that scientific judgment, methodological rigor, and human oversight remain central. This is not a traditional study-leadership role, nor is it solely a computational analytics role. It is a role for a scientifically exceptional leader who can connect portfolio strategy to scientific opportunity, frame complex questions, distinguish prognostic, predictive, causal, treatment-effect, and transportability objectives, guide multidisciplinary teams, interpret sophisticated evidence, and translate uncertainty into clear recommendations. Scope and team context You will lead the Lung and HNSCC Clinical Intelligence and RWE Strategy capability, including Strategy Leads, Evidence Product Leads, and Data Scientists. This capability sets the Therapeutic Area’s end-to-end clinical intelligence and evidence agenda across the product lifecycle, from R&D through patient-care strategy. You will translate portfolio priorities, senior stakeholder needs, and emerging scientific opportunities into a focused agenda that identifies where the team can create the greatest value. You will proactively shape the team’s contribution to the most important Therapeutic Area and asset-level priorities, moving the organization from reactive, bespoke analyses toward proactive intelligence and prospective evidence planning. You will work closely with the Lung and HNSCC Multimodal and Computational Analytics group, which provides specialist expertise in advanced methodology, machine learning, AI, molecular data, imaging, digital pathology, computational analytics, and multimodal patient classifiers. The group leads prioritized technical projects and validation activities, including classifiers intended for clinical trial deployment or potential future use in routine care. You will establish strong working relationships with Clinical Development, Biometrics, Regulatory, Translational Science, Diagnostics, Medical Affairs, Market Access, and Global Product Teams. You will set evidence priorities and scientific direction while enabling specialist teams to lead technical development and analytical delivery. Key accountabilities Portfolio strategy and evidence prioritization Maintain a clear view of Lung and HNSCC portfolio priorities, upcoming development decisions, scientific uncertainties, competitive dynamics, and senior stakeholder needs. Identify where Clinical Intelligence and Evidence capabilities can create the greatest value, focusing resources on questions that may materially influence development outcomes, patient selection, regulatory confidence, access, or investment decisions. Translate portfolio priorities and senior stakeholder needs into a focused evidence agenda, including priority intelligence initiatives, RWE strategies, analytical programs, and prospective evidence plans. Originate and prioritize high-value scientific programs addressing disease biology, patient selection, trial interpretation, treatment-effect heterogeneity, real-world outcomes, and portfolio risk. Embed evidence strategy at the asset level, ensuring that evidence needs inform development plans, trial concepts, biomarker strategies, endpoints, regulatory plans, access strategies, and prospective data collection. Balance immediate asset needs with longer-term opportunities that can strengthen decision-making across multiple programs. Ensure that insights from individual assets inform broader Therapeutic Area strategy, while portfolio priorities guide asset-level evidence plans. Scientific problem formulation and evidence architecture Translate development uncertainties into testable evidence questions, defining the decision, target population, estimand, comparator, outcomes, assumptions, and sources of uncertainty. Determine the appropriate evidence approach, using RWE, historical trial data, predictive or prognostic modeling, causal inference, treatment-effect heterogeneity, transportability, external comparators, trial simulation, and multimodal patient characterization where appropriate. Distinguish among prognostic, predictive, caus