This role can be based at AstraZeneca hubs in Boston, US or Gaithersburg, US;

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, causal, treatment-effect, and transportability questions, ensuring that analytical strategies are appropriate to the question being asked.

• Determine which data modalities provide meaningful incremental value, rather than adding complexity for its own sake.

• Define requirements for decision-ready evidence, including validation, calibration, interpretability, generalizability, clinical utility, intended use, and appropriate uncertainty assessment.

• Guide multidisciplinary programs from scientific concept through recommendation, ensuring that sophisticated analyses result in clear conclusions and actionable next steps.

• Integrate specialist contributions across clinical, statistical, computational, molecular, and real-world domains into a coherent development strategy.

Clinical development, regulatory, and evidence leadership

• Own the evidence strategy supporting major clinical development decisions, including Phase II/III transitions, trial design, patient population definition, endpoint selection, biomarker strategy, enrichment, and indication sequencing.

• Define the scientific and statistical framework for priority programs, including estimands, analysis populations, endpoint definitions, missing-data strategies, sensitivity analyses, validation plans, and interpretation of uncertainty.

• Set evidence requirements for regulatory and external decision-making, including external comparators, multimodal classifiers, transportability analyses, predictive models, treatment-effect analyses, and prospective validation.

• Assess and communicate development risk related to population representativeness, changing standards of care, recruitment feasibility, biomarker prevalence, event rates, endpoint validity, generalizability, transportability, and residual uncertainty.

• Shape evidence packages for regulatory submissions, regulatory interactions, HTA, payer evidence, and other external applications, ensuring that analyses are fit for purpose, transparent, reproducible, and appropriately validated.

• Establish the scientific alignment required to execute priority evidence programs, ensuring that relevant functions are engaged at the appropriate decision points.

Prospective evidence planning

• Define evidence requirements early in the asset lifecycle, identifying the clinical, real-world, molecular, imaging, pathology, digital, and longitudinal data needed for future decisions.

• Incorporate evidence requirements into prospective study and data-collection plans, including specimen, biomarker, endpoint, follow-up, assay, and external validation requirements.

• Plan for future generalizability and clinical utility, including representative populations, independent cohorts, intended-use settings, and missing-data considerations.

• Establish prospective evidence blueprints connecting anticipated decisions with data collection, analysis, validation, and regulatory or access use.

• Ensure that future clinical programs collect the data, samples, biomarkers, endpoints, and follow-up necessary to support transportability, clinical utility, regulatory confidence, and access strategy.

Clinical intelligence and RWE

• Lead end-to-end intelligence on standard of care, patient journeys, treatment sequencing, patterns of care, biomarkers, unmet need, outcomes, competitive activity, and evidence opportunities.

• Guide evidence strategies for treatment patterns, comparative effectiveness, external comparators, target trial emulation, treatment sequencing, post-approval evidence, and label-expansion strategy.

• Synthesize clinical trial, real-world, molecular, imaging, pathology, literature, guideline, and competitor evidence into a forward-looking Therapeutic Area perspective.

• Forecast changes in standards of care and treatment pathways, including implications for comparator selection, recruitment, trial interpretation, and future evidence needs.

• Distinguish validated evidence from exploratory signals, hypotheses, and uncertainty.

• Ensure that intelligence and evidence are directly connected to a decision, action, or portfolio need.

Multimodal and computational capabilities

• Set the clinical use case and evidence requirements for multimodal classifiers and predictive evidence products, in conjunction with the specialist technical team responsible for development and validation.

• Guide the use of clinical, real-world, molecular, imaging, pathology, and digital data to address patient stratification, prognosis, treatment response, resistance, and treatment-effect heterogeneity.

• Evaluate incremental value using discrimination, calibration, clinical utility, interpretability, generalizability, and transportability.

• Assess resilience to missing or incomplete data, including the minimum modality set required for reliable use across trials, institutions, and care settings.

• Connect computational findings with disease biology, clinically meaningful phenotypes, and treatment mechanisms.

• Enable the Multimodal and Computational Analytics group to lead prioritized technical delivery and validation, ensuring that work remains anchored in important clinical and development decisions.

AI-enabled clinical intelligence and evidence

• Identify opportunities for agentic workflows and emerging AI capabilities to improve evidence discovery, data characterization, cohort feasibility, phenotype development, literature and competitor synthesis, patient-pathway analysis, evidence-gap surveillance, analytical preparation, and quality review.

• Envision new evidence-generation workflows in which AI agents and specialized tools coordinate activities across data, literature, clinical trials, real-world evidence, and expert input.

• Define the appropriate human-AI operating model, determining which activities can be accelerated or coordinated by AI and which require clinical, scientific, epidemiological, or statistical judgment.

• Evaluate new capabilities according to their scientific and decision value, rather than technical novelty alone.

• Work with specialist technical and enterprise teams to assess and develop fit-for-purpose capabilities that improve the quality, speed, consistency, and scalability of evidence generation.

• Ensure appropriate oversight, including human review, traceability, validation, data governance, bias assessment, transparency, and clear distinction between exploratory and decision-grade outputs.

Representative scientific programs

The Senior Director will conceptualize and lead programs such as:

• Trial contextualization and benchmarking: Integrating historical trial, real-world, clinical, molecular, pathology, and imaging data to assess the generalizability of an early-phase or single-arm population to a target Phase III population.

• Multimodal patient characterization: Identifying clinically meaningful phenotypes associated with response, resistance, prognosis, or treatment effect.

• Treatment-effect heterogeneity: Distinguishing patients who are high risk from those who may derive differential benefit from a specific treatment or combination.

• External validation and transportability: Testing whether models, evidence, and phenotypes remain valid across independent populations, institutions, assays, treatment regimens, and care settings.

• Longitudinal response and treatment switching: Integrating baseline and on-treatment clinical, imaging, ctDNA, molecular, and pathology data to identify response, resistance, residual disease, or the need for treatment modification.

• Clinical utility and trial deployment: Assessing whether a classifier provides meaningful incremental value and is sufficiently rob