Senior Real World Evidence ScientistBarcelona, SpainAbout AstraZenecaAstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development, and commercialisation of prescription medicines for some of the world's most serious diseases. We're more than one of the world's leading pharmaceutical companies.Business AreaOncology is driven by speed. Here you will be backed by leadership and empowered at every level to prioritise and make ambitious moves. Be a daring decision-maker. Speak up and constructively challenge. Powered to take sensible risks based on scientific evidence. Here it's our scale, agility, and passion that makes sure we deliver fast, every time.The Oncology Real World Evidence R&D team is a new group being growing within AstraZeneca. AstraZeneca has a pedigree of experience in Real World Evidence, having developed a coherent strategy to develop and internalize rich data assets. The group is now amplifying those investments through a Real World Evidence Data Science capability.What You Will DoWe are looking for MSc/PhD level epidemiologists, bio-statisticians, biomedical data scientists, clinicians/pharmacologists, or related fields with a strong desire to learn and expand their abilities into the analysis of Real World Evidence (RWE). The ideal candidate for this role will have a deep understanding of epidemiology and will bring a consistent track record of delivering value through the use of routinely collected data from healthcare settings to provide health analytics and insights in both Public Health, Pharmaceutical Research and Development, and Commercial context.This role provides coaching, task management, and support to Programmers/Statistics/Information Scientists, promoting standard methodology across multiple domains and/or partner groups. The AstraZeneca Oncology R&D RWE group provides expert analysis and interpretation of the sophisticated biomedical data captured in electronic health records, claims data, registries, wearables, and epidemiological observations. This important work, which provides a rich window on the complicated realities of patients and diseases, is used to support the drug development process in a variety of ways, including:Analysing longitudinal health data to characterise patient journeys and outcomes across multiple modalities (genomics, clinical, imaging, etc)Sifting claims and prescription data for use patterns and to support label expansionBuilding predictive models of patient outcomesIdentifying patient subtypes
#J-18808-Ljbffr
