Real World Science, Data Scientist
At AstraZeneca we turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality.
We’re focused on the potential of science to address the unmet needs of patients around the world. We commit to those areas where we think we can really change the course of medicine and bring big new ideas to life.
The Real World Science & Digital team is a growing group within AstraZeneca’s global Evidence function which is driving the scientific use of Real World Data to accelerate the way patients access innovative medicines. The Real World Science and Digital team comprises of a core quantitative group, Digital Platform leads and Data Strategy experts providing AstraZeneca with an enterprise hub for Real World Science. AstraZeneca has a rich history in Real World Evidence, having developed a coherent strategy to develop and internalize data assets the group is now amplifying those investments through a dedicated Real World Data Science capability. Real World Data Scientists who are successful in this role will work on challenging problems, using innovative approaches to accelerate the delivery of Real World Insights and Evidence for key internal and external stakeholders.
The ideal candidate for this role will bring a proven track record of delivering value through the leverage of routinely collected data from healthcare settings to provide health analytics and insights in a range of contexts including Public Health, Pharmaceutical Research and Development and Commercial/ Payer.
They will collaborate with colleagues in Epidemiology, Statistics and Payer, giving scientific and technical guidance on study design, RW data selection and best practice in RW data utilization.
In addition, they will assist in advancing and shaping AZ’s Real World Science data strategy through the due diligence on new data providers/vendors, informatics support for data acquisitions in a range of Therapeutic Areas.
The role will promote best practice in Real World Data Science across multiple domains, and/or stakeholder groups.
- Collaborate with Payer and Epidemiology teams to maximise the value derived from large observational research data
- Deliver analyses of data from EMR, claims and primary observational data required by TA RWE strategies
- Support the development of IVS strategies and selection of optimised contact models for prioritised markets through analysis of RWD
- Provide scientific guidance on the application of Real World Evidence and observational research data to address issues across the Oncology and Biopharmaceuticals business units
- Provide technical input, options and directions to strategic decisions made by AZ observational study teams on study design, data partner selection and best practices in RWE data utilization
- Support technical teams to provide access to analytical tools and develop visual analytics to enable self-serving applications for end customers
- Provide clear technical input, options, and direction to strategic decisions on RWE platform and capability build
- Provide support for strategic decisions on AZ Medical Evidence and Observational Research external collaborations in the US and other markets
- Assist in building a capability that becomes a source of sustained competitive advantage for AZ in identifying, acquiring, integrating and mining diverse RW data from multiple geographic and healthcare system sources to support evidence generation and real-world studies
- Evaluate and assess strengths and weaknesses of external RW data sources, and potential partners for advancing the data strategy for specific therapeutic areas
- Maintain a strong insight into the capabilities of potential external partners in RWE, especially for US and emerging markets.
Education, Qualifications, Skills and Experience
- PhD or MS in data science or other advanced degree in life sciences with post doctoral or other training/work in Medical/Health Informatics or related field
- Experience in real-world evidence and familiarity with health economics/epidemiology, and quantitative science such as health outcome modelling
- Expertise in EMR/Health IT, disease registries, and insurance claims databases
- Expertise in clinical data standards, medical terminologies and controlled vocabularies used in healthcare data and ontologies (ICD9/10/ReadCode)
- Experience in supporting pharmacoepidemiology studies with proven track record of advancing approaches with data science
- Experience in Statistical Analysis Plan (SAP) generation and execution for observational studies
- Expertise in methods development and application using statistical languages such as R/Matlab/SAS/SQL/Hadoop/Python
- Experience in advanced visualisation and visual analytics of routinely collected healthcare data
- Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data
- A history of patient care or equivalent background of working at a patient care setting that allows the candidate to bring medical perspective into real-world evidence generation and observational studies
- Demonstrated ability to build long-term relationships with stakeholders 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 data science projects
- Strong track record of delivering large, cross functional projects
- Experience working in a global organization and delivering global solutions
- Use of Machine Learning and Artificial Intelligence in the generation of hypotheses within Real World Data