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 company'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.
Typical Accountabilities:
Collaborate with Payer and Epidemiology teams to maximize 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 optimized contact models for prioritized 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 COMPANY 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 company 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 company 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:
Essential:
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.
5 years of experience
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
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. R is strongly preffered, and SAS a plus.
Experience in advanced visualisation and visual analytics of routinely collected healthcare data
Desirable:
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
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
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