Associate Director, Oncology Clinical Intelligence and Real World Evidence

WHY JOIN US?

At AstraZeneca, we are dedicated to being a Great Place to Work, where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. With an ambitious goal of delivering 20 new medicines by 2030, there is no better place to make a difference in medicine, for patients, and in society.

Join us on this exciting journey to pioneer the future of healthcare. Our inclusive culture champions diversity and collaboration, and we are always committed to lifelong learning, growth, and development. As part of our team, you'll contribute to groundbreaking advancements that will shape a healthier future for people everywhere. Be a catalyst for change at AstraZeneca, where your work will have a real impact on improving people's lives.

Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face-to-face in our offices 3 days a week. Our head office is purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects.

Introduction to role:

Are you ready to turn complex clinical and real-world data into evidence that shapes pivotal decisions in Hematology/Oncology? Do you want your analytics to directly influence Phase 3 investments, trial design, and patient access?

In this role, you will operate where evidence generation meets hands-on analytics, partnering with stakeholders to pinpoint the highest-value evidence gaps and leading the delivery that closes them. You will move seamlessly from portfolio-level prioritization with Global Product Teams to building rigorous multimodal patient models or external control arm analyses that inform development and access.

Hematology is growing rapidly. As the technical specialist for clinical intelligence and real-world evidence, you will translate complex data into decision-ready insights, build persistent intelligence that compounds across use cases, and help steer how the portfolio advances for patients.

Accountabilities:

• Evidence Gap Prioritization: Partner with Hematology/Oncology stakeholders and Global Product Teams to identify and prioritize evidence needs across the product lifecycle, including Phase 3 investment decisions, subpopulation discovery, and trial design.
• AI and Causal Inference Analytics: Apply machine learning and causal inference to deliver robust answers on patient stratification, external control arm construction, prognostic risk adjustment, and treatment effect heterogeneity, ensuring analyses meet regulatory and HTA expectations.
• Multimodal Patient Models: Deliver and validate patient-level models that integrate clinical, genomic, imaging, and real-world data for deployment in clinical trials or routine care, with emphasis on reproducibility and rigorous validation.
• Decision-Ready Insights: Turn internal and competitor trial data, alongside real-world data, into clear insight on standard of care, patient pathways, benchmarking, and unmet need to sharpen development and access strategies.
• Platform Enablement: Work with platform and tooling teams to bring new tools, agents, and experimental approaches into evidence generation, and build Hematology-specific intelligence that persists within the Phase 3 Investment Decision Intelligence Foundation.
• Cross-Functional Collaboration: Connect strategic evidence needs with technical delivery, aligning outputs to development, regulatory, and access milestones; collaborate with Translational Science and Clinical Development to incorporate novel signals such as digital endpoints, pathology AI, and biomarker panels.

Essential Skills/Experience:

• Advanced degree (PhD or equivalent) in a quantitative discipline - epidemiology, biostatistics, computational biology, machine learning, health data science, or a related field.

• Strong methodological foundation in causal inference and observational study design, including propensity score methods, instrumental variables, target trial emulation, and comparative effectiveness research.

• Hands-on experience with machine learning and multimodal modeling, including supervised and unsupervised methods, deep learning for imaging or molecular data, and integration of heterogeneous data types into patient-level models.

• Experience building or contributing to external control arms, trial simulators, or prognostic models using real-world and/or clinical trial data.

• Understanding of regulatory and health technology assessment (HTA) evidence standards, with the ability to design analyses that meet the evidentiary bar for submissions and payer engagement.

• Proficiency in Python, R, and SQL, and familiarity with cloud-based analytics environments.

Desirable Skills/Experience:

• Domain expertise in hematology or oncology and familiarity with disease-specific endpoints, pathways, and standards of care.

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