Senior Real World Evidence Scientist
3 weeks ago
About Astrazeneca
AstraZeneca 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. But we’re more than one of the world’s leading pharmaceutical companies.
Business area
Oncology 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 Do
We 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 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 expansion Building predictive models of patient outcomes Identifying patient subtypes ( via biomarkers) for possible therapy development Building synthetic and external control arms to support the interpretation of clinical studies Development of algorithms for better diagnosis and identification of patients Searching for evidence of adverse effects in medical histories Using federated networks of electronic health records for patient identification and recruitment Using real world evidence to support pragmatic and hybrid trial designs Partnering with external organisations to generate custom real-world datasetsRequirements
Masters Degree in relevant field ( would be preferred) Relevant experience Use of statistical and scripting languages such as R, Python and SQL Clinical trials and recruitment, especially the application of synthetic control arms Experience in supporting pharmacoepidemiology studies with proven track record of advancing approaches with data science Demonstrated ability to build long-term relationships with partners at senior levels, understand relevant scientific/business challenges at a deep level and translate into a programme of informatics activities to deliver defined value Ability to lead & manage multi-disciplinary epidemiological projects Strong background of delivering large, cross functional projects Experience working in a global organisation and delivering global solutionsDesirable Skills
Health analytics and data mining of routinely collected healthcare data The application of genomics in clinical care or translational medicine Health economics and quantitative science such as health outcome modelling Data science, machine learning and construction of predictive models Clinical data standards, medical terminologies and healthcare ontologies Work in a patient care or similar setting, that would allow the candidate to bring medical perspective into real-world evidence generation Experience design and implementing pragmatic clinical trialsDate Posted
03-Jun-2024Closing Date
04-Jul-2024-
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