Senior Director, Oncology Clinical Intelligence & Evidence

Introduction to role

Shape the evidence that determines which medicines reach patients—and how quickly. As Senior Director, Oncology 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.

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 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.

Key accountabilities

• Maintain a clear view of Lung and HNSCC portfolio priorities, upcoming development decisions, scientific uncertainties, competitive dynamics, and senior stakeholder needs.
• 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.
• 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.
• 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 evidence requirements early in the asset lifecycle, identifying the clinical, real-world, molecular, imaging, pathology, digital, and longitudinal data needed for future decisions.
• 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.
• 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.
• 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.
• 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.

Representative scientific programs

• 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 robust, interpretable, and feasible for trial enrichment or future clinical use.
• Agentic clinical intelligence and evidence workflows: Designing and evaluating AI-enabled workflows in which agents and specialized tools coordinate evidence discovery, literature and competitor synthesis, cohort and phenotype development, patient-pathway analysis, evidence-gap surveillance, analytical preparation, and quality review.
• Scenario and decision analysis: Evaluating how population mix, phenotype prevalence, treatment response, missingness, and other assumptions affect trial interpretation and development risk.
• Reverse translation: Connecting predictive features and patient phenotypes to disease biology, treatment mechanisms, resistance pathways, and new therapeutic opportunities.

Essential skills and experience

• Scientific architecture: Demonstrated ability to design multidisciplinary evidence programs from first principles, integrating clinical development questions with appropriate causal, predictive, statistical, computational, and biological approaches.
• Therapeutic-area leadership: Significant experience setting and delivering clinical intelligence, RWE, clinical development, or evidence strategy across a complex oncology portfolio.
• Portfolio strategy: Demonstrated ability to understand portfolio priorities, anticipate senior stakeholder needs, identify where evidence capabilities can create the greatest value, and shape proactive contributions to important scientific and business priorities.
• Clinical development decision-making: Experience applying complex evidence to Phase II/III transitions, trial design, patient enrichment, biomarker strategy, indication sequencing, or development-risk assessment.
• Oncology expertise: Strong understanding of lung cancer and/or HNSCC biology, treatment pathways, biomarkers, patient segmentation, clinical development, and evolving standards of care.
• Evidence-generation credibility: Deep expertise in RWE, epidemiology, biostatistics, clinical development, health outcomes research, clinical intelligence, or a related discipline.
• Methodological judgment: Strong understanding of observational study design, causal inference, comparative effectiveness, external comparators, target trial emulation, longitudinal outcomes, and transportability.
• Predictive and causal fluency: Demonstrated ability to distinguish prognostic, predictive, causal, treatment-effect, and generalizability questions.
• Multimodal fluency: Understanding of how clinical, molecular, imaging, pathology, digital, and real-world data can be integrated to address patient stratification, biomarkers, prognosis, prediction, and treatment response.
• AI-enabled workflow and capability design: Ability to envision and evaluate how AI, agentic workflows, intelligent tools, and emerging technologies can transform clinical intelligence, RWE, clinical development, or multimodal evidence generation.
• Model and evidence evaluation: Experience evaluating discrimination, calibration, clinical utility, interpretability, external validation, generalizability, transportability, and missing-data resilience.
• Prospective evidence planning: Experience defining data, sample, biomarker, endpoint, follow-up, and external validation requirements before data generation.
• Cross-functional influence: Proven ability to work effectively with Clinical Development, Biometrics, Regulatory, Translational Science, Diagnostics, Medical Affairs, Market Access, and Global Product Teams.
• Matrix leadership: Demonstrated ability to lead multidisciplinary teams and influence without direct authority.
• Scientific communication: Ability to translate complex evidence and uncertainty into clear recommendations for technical, clinical, executive, regulatory, and commercial audiences.
• Responsible innovation: Understanding of evidence governance, reproducibility, data provenance, bias, privacy, interpretability, traceability, and appropriate human oversight.

Education and experience

• Advanced degree, such as MD, PhD, PharmD, MSc, or equivalent, in epidemiology, biostatistics, health economics, clinical science, oncology, data science, computer science, or another relevant discipline.
• Typically 12 or more years of relevant experience in clinical development, RWE, epidemiology, biostatistics, health outcomes research, clinical intelligence, data science, or a related discipline.
• Demonstrated experience leading multidisciplinary teams and influencing important clinical development, regulatory, portfolio, access, or commercial decisions.
• Evidence of scientific excellence through influential publications, presentations, regulatory contributions, implemented analytical approaches, or other meaningful contributions to the field.

Desirable skills and experience

• Experience across both lung cancer and HNSCC or multiple thoracic and head and neck oncology settings.
• Experience with immuno-oncology, targeted therapies, biomarker-defined populations, antibody-drug conjugates, radioconjugates, or other innovative oncology modalities.
• Experience with target trial emulation, causal machine learning, treatment-effect heterogeneity, external controls, trial simulation, transportability, multimodal classifiers, or longitudinal multimodal data.
• Experience with digital pathology, imaging, genomics, molecular data, ctDNA, radiomics, or diagnostic development.
• Experience with prospective validation, clinical utility, trial deployment, or regulatory strategy for predictive or multimodal evidence products.
• Experience with generative AI, AI agents, retrieval-augmented systems, natural language processing, workflow orchestration, automation, or experimental AI-supported approaches applied to clinical intelligence, RWE, clinical development, or scientific workflows.