Fall Intern: Data Analysis and Education Policy

Overview 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 drawingmeaningful interpretationsfrom state andnationaleducation data sets; using statistical modeling to estimate causal effects; usingweb scraping to construct novel education data sets; applyingtext analysis, machine learning, and other techniques to analyze unstructured data; and creating visualizations and written reports to communicate results. Candidates are expected to: be highly interested in learning more about education policy; have keen attention to detail and be highly effective at communication; be highly proficient in Python and/or R. have a strongunderstanding of statistics; have a foundational understanding of machine learning; be comfortable working independently when given a project; and be able andeager to learn new skills on the job. Applicants who go above and beyond will: have taken coursework to understand, or have work experience in, education policy; and demonstrate fluency in both Python and R, and potentially other programming languages; have a strong understanding of regression and causal analysis; and have experience applying machine learning and/or natural language processing to unstructured,messy data sets. The ideal candidate for this position will have a passion for understanding K–12 American education through multiple types of data, have a general STEM background with a foundational understanding of data science, and be driven by open-minded, intellectual curiosity. Candidates who are able to participate in the program on an in-person basis for 30-40 hours a week are encouraged to apply. Competitive candidates will generally have a GPA of 3.5 or higher from a top-ranking college or university. #J-18808-Ljbffr AEI