Are you ready to turn complex real-world data into evidence that shapes clinical development decisions in Cardiovascular, Renal and Metabolic disease? Join us to apply epidemiology, biostatistics and AI to answer high-value scientific questions that move medicines forward.
In this role, you will be part of a cross-functional team bridging Clinical Development, Biostatistics and Data Science to design and deliver observational studies at scale. Your work will inform trial strategy, refine patient selection and endpoints, and improve the speed and quality of decision-making across the CVRM portfolio. Can you see yourself translating sophisticated analytics into clear, decision-relevant insights that influence the path of our pipeline?
Accountabilities:
Evidence Generation and Study Delivery: Design and deliver observational studies and RWE projects that address priority scientific and clinical development questions across CVRM, from protocol and analysis plan through to results interpretation and dissemination.
Decision-Ready Insights: Translate findings into clear, evidence-based recommendations that guide study design, development milestones and lifecycle decisions.
End-to-End Analytical Execution: Build and validate cohort and phenotyping definitions; conduct data feasibility assessments; implement statistical analyses; and report results with transparency and reproducibility.
Scientific and Technical Expertise: Apply robust observational study design, epidemiology and biostatistics; select and implement approaches such as comparative effectiveness, longitudinal analysis, causal inference, patient characterization and predictive analytics aligned to the research question
Real-World Data Mastery: Work across claims, electronic health records, registries and linked datasets; assess suitability and limitations; and clearly document assumptions, methods and outputs aligned to internal scientific standards.
AI-Enabled and Scalable Analytics: Deploy AI and machine learning to accelerate phenotyping, endpoint identification, workflow automation and analytical standardisation; contribute reusable code, pipelines, templates and standards to increase consistency and efficiency.
Method Innovation and Evaluation: Scout, evaluate and help adopt new methods, tools and technologies that strengthen delivery and analytical quality.
Cross-Functional Collaboration: Partner closely with Clinical Development, Biostatistics, Data Science and other stakeholders to align objectives, methods and outputs with program decisions; communicate technical concepts to specialist and non-specialist audiences.
Quality, Standards and Continuous Improvement: Uphold rigorous scientific quality, reproducibility and transparency; follow best practices for documentation, coding, quality control and study conduct; share knowledge and help evolve team capabilities and standards.
Essential Skills/Experience:
Advanced degree such as PhD or MSc in epidemiology, biostatistics, data science, public health, or a related discipline.
Strong understanding of observational study design, epidemiologic methods, and the interpretation of real-world data.
Experience applying machine learning or AI methods in healthcare data or related analytical settings.
Practical experience working with real-world data assets such as claims, electronic health records, registries, or linked healthcare datasets.
Experience applying quantitative methods to support evidence generation, including some of: comparative effectiveness research, longitudinal data analysis, causal inference, phenotyping, or predictive modelling.
Strong programming and data analysis skills in R, Python, and/or SQL. - Ability to manage and deliver multiple analyses or projects with a focus on quality, timelines, and scientific robustness.
Strong written and verbal communication skills, with the ability to explain technical findings clearly.
Advanced English.
Desirable Skills/Experience:
Experience developing reusable analytical code, tools, or workflows. - Familiarity with privacy-preserving analytics, federated approaches, or working across diverse data environments. Experience with multimodal real-world data or linked datasets.
Experience in Cardiovascular, Renal and Metabolic (CVRM) disease areas. 6.
Why AstraZeneca: Here, science meets advanced analytics to unlock the next wave of medical breakthroughs. You will collaborate in unexpected teams—clinicians, statisticians, data scientists and engineers in the same room unleashing bold thinking—while working with cutting-edge data, methods and technology that directly influence development decisions.
We combine a strong pipeline with an environment that encourages curiosity, publication, and lifelong learning; we value kindness alongside ambition so you can stretch into new methods, tackle uncertainty and still feel supported. Your contributions will scale beyond a single study to accelerate medicines for patients who need them most.
Call to Action: If you are ready to shape pivotal decisions with rigorous real-world evidence and scalable analytics, apply today to turn data into impact for patients and our CVRM pipeline!
Date Posted
10-jun-2026Closing Date
30-jun-2026AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.