Senior Director, Oncology Clinical Intelligence & Evidence

AstraZeneca•Cambridge, MA

8d•$203,214 - $304,820•Hybrid

About The Position

The Senior Director will lead the Lung and HNSCC Therapeutic Area’s Clinical Intelligence and Real-World Evidence (RWE) strategy, setting the direction for how evidence is generated, interpreted, and applied across the portfolio and product lifecycle. The role translates portfolio priorities, senior stakeholder needs, and scientific opportunities into an evidence agenda focused on questions with meaningful clinical, scientific, and business value. Evidence generated will inform clinical development, Phase II/III transitions, trial design, patient stratification, biomarker strategy, regulatory confidence, medical strategy, market access, and portfolio investment. The role leads a capability comprising Strategy Leads, Evidence Product Leads, and Data Scientists, and works closely with the Lung and HNSCC Multimodal and Computational Analytics group. It establishes priorities and scientific direction while enabling specialist teams to lead technical development and analytical delivery. Key partners include Clinical Development, Biometrics, Regulatory, Translational Science, Diagnostics, Medical Affairs, Market Access, and Global Product Teams. The Senior Director will formulate scientific questions, select fit-for-purpose evidence approaches, guide multidisciplinary programs, and translate complex evidence and uncertainty into clear recommendations. The role spans clinical intelligence, RWE, prospective evidence planning, clinical development and regulatory evidence, multimodal and computational capabilities, and the responsible use of AI-enabled and agentic workflows. It requires scientific judgment, methodological rigor, human oversight, and a focus on decision value, adoption, and scalable impact. The position can be based at AstraZeneca hubs in Boston or Gaithersburg. Office working averages a minimum of three days per week, with individual flexibility. The annual base pay range is $203,213.60–$304,820.40, with a short-term incentive bonus opportunity and eligibility for an equity-based long-term incentive program.

Requirements

Advanced degree, such as MD, PhD, PharmD, MSc, or equivalent, in a relevant discipline such as epidemiology, biostatistics, health economics, clinical science, oncology, data science, or computer science.
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.
Significant experience setting and delivering clinical intelligence, RWE, clinical development, or evidence strategy across a complex oncology portfolio.
Demonstrated ability to design multidisciplinary evidence programs from first principles, integrating clinical development questions with causal, predictive, statistical, computational, and biological approaches.
Experience understanding portfolio priorities, anticipating senior stakeholder needs, and shaping proactive evidence contributions to scientific and business priorities.
Experience applying complex evidence to Phase II/III transitions, trial design, patient enrichment, biomarker strategy, indication sequencing, or development-risk assessment.
Strong understanding of lung cancer and/or HNSCC biology, treatment pathways, biomarkers, patient segmentation, clinical development, and evolving standards of care.
Deep expertise in RWE, epidemiology, biostatistics, clinical development, health outcomes research, clinical intelligence, or a related discipline.
Strong understanding of observational study design, causal inference, comparative effectiveness, external comparators, target trial emulation, longitudinal outcomes, and transportability.
Ability to distinguish prognostic, predictive, causal, treatment-effect, and generalizability questions.
Understanding of integrating clinical, molecular, imaging, pathology, digital, and real-world data for patient stratification, biomarkers, prognosis, prediction, and treatment response.
Ability to envision and evaluate AI, agentic workflows, intelligent tools, and emerging technologies for clinical intelligence, RWE, clinical development, or multimodal evidence generation.
Experience evaluating discrimination, calibration, clinical utility, interpretability, external validation, generalizability, transportability, and missing-data resilience.
Experience defining data, sample, biomarker, endpoint, follow-up, and external-validation requirements before data generation.
Proven ability to work effectively across Clinical Development, Biometrics, Regulatory, Translational Science, Diagnostics, Medical Affairs, Market Access, and Global Product Teams.
Demonstrated ability to lead multidisciplinary teams and influence without direct authority.
Ability to communicate complex evidence and uncertainty clearly to technical, clinical, executive, regulatory, and commercial audiences.
Understanding of evidence governance, reproducibility, data provenance, bias, privacy, interpretability, traceability, and appropriate human oversight.
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.

Nice To Haves

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.

Responsibilities

Lead the Lung and HNSCC Clinical Intelligence and RWE Strategy capability and set the Therapeutic Area’s end-to-end evidence agenda across the product lifecycle.
Translate portfolio priorities, stakeholder needs, and scientific opportunities into focused evidence priorities, programs, and prospective evidence plans.
Identify and prioritize scientific programs that can influence development outcomes, patient selection, regulatory confidence, access, investment, and portfolio risk.
Embed evidence strategy in asset development plans, including trial concepts, biomarker strategies, endpoints, regulatory plans, access strategies, and prospective data collection.
Translate development uncertainties into testable evidence questions, specifying the decision, target population, estimand, comparator, outcomes, assumptions, and uncertainties.
Select and guide appropriate evidence approaches, including RWE, historical trial data, causal inference, predictive or prognostic modeling, external comparators, trial simulation, transportability, and multimodal patient characterization.
Define decision-ready evidence requirements, including validation, calibration, interpretability, generalizability, clinical utility, intended use, and uncertainty assessment.
Guide multidisciplinary programs from scientific concept through recommendation, integrating clinical, statistical, computational, molecular, and real-world expertise.
Own evidence strategy for major clinical development decisions, including Phase II/III transitions, trial design, population definition, endpoint selection, biomarker strategy, enrichment, and indication sequencing.
Define scientific and statistical frameworks for priority programs, including estimands, analysis populations, endpoint definitions, missing-data strategies, sensitivity analyses, validation plans, and interpretation of uncertainty.
Set evidence requirements and shape evidence packages for regulatory submissions and interactions, HTA, payer evidence, and other external applications.
Assess and communicate development risks involving representativeness, standards of care, recruitment, biomarker prevalence, event rates, endpoint validity, generalizability, transportability, and residual uncertainty.
Plan prospective evidence needs, including clinical, real-world, molecular, imaging, pathology, digital, and longitudinal data, and associated specimens, biomarkers, endpoints, follow-up, assays, and validation.
Lead intelligence on standards of care, patient journeys, treatment sequencing, patterns of care, biomarkers, unmet need, outcomes, competitive activity, and evidence opportunities.
Guide RWE strategies for treatment patterns, comparative effectiveness, external comparators, target trial emulation, treatment sequencing, post-approval evidence, and label expansion.
Synthesize trial, real-world, molecular, imaging, pathology, literature, guideline, and competitor evidence into a forward-looking Therapeutic Area perspective.
Set clinical use cases and evidence requirements for multimodal classifiers and predictive evidence products with the specialist technical team.
Evaluate classifiers and evidence products for incremental value, discrimination, calibration, clinical utility, interpretability, generalizability, transportability, and resilience to missing data.
Identify and assess opportunities for AI-enabled and agentic workflows in evidence discovery, cohort feasibility, phenotype development, literature and competitor synthesis, patient-pathway analysis, evidence-gap surveillance, analytical preparation, and quality review.
Define appropriate human–AI operating models and ensure oversight, traceability, validation, data governance, bias assessment, transparency, and distinction between exploratory and decision-grade outputs.
Build effective cross-functional relationships and establish scientific alignment at relevant decision points.
Lead and influence multidisciplinary teams, including Strategy Leads, Evidence Product Leads, and Data Scientists, while enabling specialist teams to lead technical development and delivery.