Associate Director, Real World Evidence Data Scientist

Job Overview:

The ideal candidate for this role will bring a proven track record of delivering value through the utilization of routinely collected data from healthcare settings, providing health analytics and insights in various contexts, including Public Health, Pharmaceutical Research and Development, and Commercial/Payer sectors. Collaborate with Epidemiology and Statistics colleagues to provide scientific and technical mentorship on study design and RW data utilization. The role will promote standard approaches in Real-World Data Science across different domains and collaborating groups.

Key Responsibilities:

• Collaborate with Epidemiology and HEOR teams to improve the value derived from a variety of real-world data sources, including EMR, claims, and primary observational data.
• Support collaborators within Medical Affairs by providing access to analytical tools and developing visual analytics to enable self-serving applications for end customers.
• Provide clear technical input, options, and direction to RWD analysis and utilization supporting RWE and insights generation.
• Maintain a strong insight into the capabilities of in-house RWD to facilitate data source selection for RWE and insights generation.
• Help build a capability that becomes a source of sustained competitive advantage for AstraZeneca in identifying, acquiring, integrating, and mining diverse RW data from multiple geographic and healthcare system sources to support evidence generation and real-world studies.

Essential Education, Skills & Experience:

• PhD or MS in data science or other advanced degree in life sciences such as epidemiology, or other training/work in Medical/Health Informatics/Biostatistics, or a related field.
• Expertise in data analysis, data mining, data visualization, methods development and application using statistical languages such as R, SAS, SQL, or Python.
• Expertise in advanced visualization platform and visual analytics development, such as Power BI and R Shiny.
• Experience in real-world evidence and familiarity with health economics, epidemiology, observational study methodologies, and quantitative sciences such as health outcome modeling.
• Expertise in EMR/Health IT, disease registries, and insurance claims databases.
• Experience in Statistical Analysis Plan (SAP) generation and execution for observational studies.

Desired Experience & Skills:

• Expertise in clinical data standards, medical terminologies, and controlled vocabularies used in healthcare data and ontologies (ICD-9/10, NDC, HCPCS).
• Experience in supporting pharmacoepidemiology studies with a proven track record of advancing approaches with data science.
• Expertise in data mining approaches within healthcare settings to generate insights from routinely collected healthcare data.
• A history of patient care or equivalent background in a patient care setting that allows the candidate to bring a medical perspective into real-world evidence generation and observational studies.
• Demonstrated ability to build positive relationships, understand key challenges, and develop beneficial informatics projects.
• Ability to lead and 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.
• Familiarity with the use of Machine Learning and Artificial Intelligence in the generation of hypotheses within Real-World Data

Date Posted

Closing Date

AstraZeneca 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.