Associate Director – Real World Evidence Data Scientist
| Company | AstraZeneca |
|---|---|
| Location | Mississauga, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior |
Requirements
- 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.
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.
Preferred Qualifications
- 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.