HEOR Intern, Real-World Evidence -
Location: Remote, United States
Remote, United States
Job Type: intern
Division: Precision AQ
Business Unit: Evidence Solutions
Requisition Number: 6623
Essential duties include but are not limited to:
Help streamline internal processes – work directly with the Precision leadership to improve processes and efficiency; investigate optimal research techniques and implementation strategies; update existing tools and documentation to increase adherence to best practices; support development and refinement of interactive tools that use existing real-world evidence analyses.
Support ongoing research projects – assist with literature reviews on diverse healthcare topics; help with real-world evidence studies including protocol development, data programming, and results; develop interactive summaries of real-world evidence analyses; help interpret and communicate quantitative and qualitative findings to internal and external audiences; contribute to reports, results tables, presentations or peer-reviewed manuscripts; participate in client-facing activities; provide administrative project support.
Qualifications
Required:
- Enrolled in a public health (epidemiology, biostatistics, bioinformatics), economics, data science, life sciences, or healthcare related undergraduate, or preferably, graduate program at an academic institution at the time of recruitment.
Other required:
- Proven interest in public health, life sciences, or pharmaceutical research and analytics
- Research experience in epidemiology, public health, health services research, health policy, economics or related fields
- Foundational knowledge of epidemiologic concepts such as observational study designs, measures of association, and common sources sources of bias and confounding
- Familiarity / competency with statistical programming tool such as R, SQL, SAS or similar platform
- Ability to work effectively both individually and as part of a team
- Detail oriented with exceptional interpersonal and time management skills
- Keen interest in learning and sharing knowledge
- Strong academic record
- Proficiency with Microsoft Office
Preferred:
- Enrolled in life sciences related advanced degree program
- Strong understanding of the scientific method; formal training in quantitative methodologies
- Familiarity with interactive data visualization tools such as R Shiny, Power BI, Tableau, or equivalent platforms
- Familiarity with working in virtual AWS environment, Git, and Github
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