Job Description

The appropriate use of Real-World Data (RWD) across the full cycle of drug development is essential in the pharmaceutical industry. Pharma R&D is increasingly using RWD in the pre-market setting to inform clinical development programs and support regulatory submissions. At AstraZeneca (AZ), the clinical data sciences and real-world evidence (RWE) team within the broader AZ Oncology Data Science organization ensures all Phase 2/3 clinical trials, from investment decisions through regulatory filing and commercialization are informed with robust Real World Evidence (RWE). While our day-to-day work generates RWE for internal decision-making on clinical trial study design and interpretation (e.g., control arm selection, expected control arm performance, study population definitions, trial interim analyses), our work is increasingly expected to impact regulatory submissions, providing supportive and eventually pivotal evidence. Collected from various sources such as electronic health records and medical claims for non-research purposes, RWD has limitations versus RCTs. As such, we strive to use thorough study design and leverage the best methods to control numerous sources of bias that arise with non-randomized study designs. Furthermore, in oncology drug development, databases of NGS-tested patients rich in molecular data coupled with clinical data have huge potential, but require additional development and application of novel methods to understand their relevance to AZ trials. This postdoc position has a duration of up to three years. The postdoc will design and implement several demonstration projects, testing approaches to correct or minimize the impact of various biases we encounter in our day-to-day use of claims, electronic health records, and multi-modal data. Results from these methods will be compared to AZ's status-quo analytic approaches and validated against historical clinical trials' SoC arms. You will have the opportunity to formulate and address key scientific questions motivated by AstraZeneca’s oncology portfolio strategy. These questions will drive innovative research of broad interest to the pharmacoepidemiology and clinical-genomics scientific communities. You will produce high-impact publications on innovative research questions in the fields of pharmacoepidemiology and causal inference and simultaneously advance AstraZeneca’s use of RWE to accelerate and streamline drug development. Furthermore, you will have access to extensive sources of RWD and clinical trial data, and the support of teams with in-depth knowledge of data sources, clinical expertise, and mentorship from internal and external methods experts. You’ll join a highly motivated, fast-paced team of epidemiologists and data scientists committed to rigorous research, bringing novel RWD insights to AstraZeneca’s Oncology pipeline. Your experience will be enriched through collaboration with a diverse internal community, including statistical innovation, applied data science, artificial intelligence, clinical teams, and the broader community of AstraZeneca postdocs. In addition to the team at AstraZeneca, you will have the opportunity to be mentored by two academic advisors, Dr. Issa Dahabreh from the T.H. Chan Harvard School of Public Health, and Dr. Camille Maringe at the London School of Hygiene and Tropical Medicine. Essential skills include solid understanding of epidemiological study design, experience or knowledge of working with Real World Data such as claims, electronic health records, or clinical trial data, significant experience with bias and methods to correct bias such as propensity score weighting/matching, instrumental variable analysis, matching adjusted indirect comparisons, familiarity with approaches for generalizability and transportability, and experience coding in R or similar. Essential qualifications include a PhD in Epidemiology, Pharmacoepidemiology, Biostatistics, Statistical Genetics, Clinical Informatics, Computer Science, or Health Informatics. Desirable skills include oncology knowledge, understanding of drug development, causal inference, Bayesian methods, quantitative bias analysis, prior pharma or clinical trials experience, or engagement with regulators. Personal characteristics sought are collaborative, innovative, curious, and patient-focused. Compensation is based on years of experience after PhD, ranging from $107,500 to $120,000 plus short-term incentive, with some relocation supplements at employer discretion.

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