Fall Intern: Data Analysis and Education Policy
About The Position
The education policy team’s data intern will support the team’s cutting-edge research at the intersection of data, education policy, and technology. The data intern will research a variety of topics, including by: analyzing and drawing meaningful interpretations from state and national education data sets; using statistical modeling to estimate causal effects; using web scraping to construct novel education data sets; applying text analysis, machine learning, and other techniques to analyze unstructured data; and creating visualizations and written reports to communicate results. AEI internships offer a unique opportunity for undergraduates, graduate students, and recent graduates to gain experience in research, writing, business, and communications at one of the nation’s leading think tanks. The fall program dates are either Tuesday, September 8, to Friday, December 4, or Tuesday, September 15, to Friday, December 11.
Requirements
- Highly interested in learning more about education policy
- Keen attention to detail and highly effective at communication
- Highly proficient in Python and/or R
- Strong understanding of statistics
- Foundational understanding of machine learning
- Comfortable working independently when given a project
- Able and eager to learn new skills on the job
- GPA of 3.5 or higher from a top-ranking college or university
Nice To Haves
- Taken coursework to understand, or have work experience in, education policy
- Demonstrate fluency in both Python and R, and potentially other programming languages
- Strong understanding of regression and causal analysis
- Experience applying machine learning and/or natural language processing to unstructured, messy data sets
- Passion for understanding K–12 American education through multiple types of data
- General STEM background with a foundational understanding of data science
- Driven by open-minded, intellectual curiosity
Responsibilities
- Analyzing and drawing meaningful interpretations from state and national education data sets
- Using statistical modeling to estimate causal effects
- Using web scraping to construct novel education data sets
- Applying text analysis, machine learning, and other techniques to analyze unstructured data
- Creating visualizations and written reports to communicate results
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What This Job Offers
Career Level
Intern
Education Level
No Education Listed
