Real World Evidence Associate DirectorBarcelona, SpainJob 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. But we’re more than one of the world’s leading pharmaceutical companies.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. 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 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.We are looking for a 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 in the use of RWD. 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 individual will also be responsible to the ideation of new methods and applications of RWE for new clinical development challenges and will be responsible for supporting new regulatory interactions using RWD.The AstraZeneca Oncology R&D RWE group provide expert analysis and interpretation of the complex 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:Supporting clinical project teams 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 programInteract 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. via biomarkers) for possible therapy developmentBuilding synthetic control arms to support the interpretation of clinical studiesDevelopment of algorithms for better diagnosis and identification of patientsSearching for evidence of adverse effects in medical historiesEstimating the number of eligible patients for clinical trials from databases and literatureUsing federated networks of electronic health records for patient identification and recruitmentUsing real world evidence to support pragmatic and hybrid trial designsPartnering with external organizations to generate custom real world datasetsMinimum Qualifications:Master's degree + 5+ years of relevant experienceExperience in supporting a multidisciplinary team build a research objective that can be met with RWDExperience in the use and application of RWD to support clinical decision makingHealth analytics and data mining of routinely collected healthcare dataUse of statistical and scripting languages such as R, Python and SQLClinical trials and recruitment, especially the application of synthetic control armsThe application of genomics in clinical care or translational medicineHealth economics or epidemiology, and quantitative science such as health outcome modellingDesirable SkillsPhD DegreeData science, machine learning and construction of predictive modelsClinical data standards, medical terminologies and healthcare ontologiesWork in a patient care or similar setting, that would allow the candidate to bring medical perspective into real-world evidence generationExperience designing and implementing pragmatic clinical trialsKnowledge of Oncology and Pharmaceutical developmentDate Posted 11-abr-2024Closing DateAstraZeneca 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.
