.Location: Barcelona, Spain (on-site) - 3 days working from the office and 2 days working from home.Job DescriptionAstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines for some of the world's most serious diseases.Oncology is driven by speed. Here you will be backed by leadership and empowered at every level to prioritize and make ambitious moves. Be a daring decision-maker. Speak up and constructively challenge. Powered to take sensible risks based on scientific evidence.The Oncology Real World Evidence R&D team is a new group growing within AstraZeneca. We are looking for quantitative epidemiologists, bioinformaticians, health informaticians, biomedical data scientists, clinicians/pharmacologists, bio-statisticians or related fields with a strong desire to learn and expand their abilities into the analysis of Real World Evidence (RWE).The role holder will be a subject matter expert on the use of Real World Data and its capabilities. The role holder will transform real-world clinical data into concrete insights for clinical development using statistical methods and innovative data visualizations to support decisions.The AstraZeneca Oncology R&D RWE group provides expert analysis and interpretation of complex biomedical data captured in electronic health records, claims data, registries, wearables and epidemiological observations. This important work supports the drug development process in various ways, including:Supporting clinical project teams to understand the benefits of RWD and support them in their clinical designDeveloping close connections with biometrics and clinical teams to develop a strategy for RWD use within a drug development programInteracting with senior stakeholders to ensure the value of RWD is understood and supported within a Research and Development settingAnalyzing longitudinal health data to characterize patient journeys and outcomesSifting claims and prescription data for use patterns and to support label expansionBuilding predictive models of patient outcomesIdentifying patient subtypes (e.G