Summer 2027: Data Analytics Intern

Role overview Help turn product and user data into clear findings that inform decisions about digital learning experiences. This summer internship covers data preparation, exploratory analysis, reporting, and measurement of product engagement and learning outcomes, in collaboration with data, engineering, and product teams.

Responsibilities - Collect, clean, and organize datasets so they are accurate and useful across teams. - Explore student, educator, and product usage data to surface patterns and potential insights. - Build or support dashboards, reports, and visualizations for technical and non-technical audiences. - Assist with one-off analyses, experiments, and product performance reporting. - Document data definitions, analytical approaches, and findings in presentations or internal materials. - Track performance indicators related to product health, engagement, and learning outcomes.

Requirements - Be enrolled in an undergraduate or graduate degree program and plan to return to school in fall 2027. - Be available full time from June 7 through August 13, 2027, and authorized to work and reside in the United States during the internship. - Communicate clearly and be able to synthesize information, identify themes, and explain findings. - Have an interest in K–12 education technology and how data can inform learning experiences.

Nice to have - Working knowledge of SQL and exposure to visualization platforms such as Tableau, Power BI, or Looker. - Experience with statistical analysis, data wrangling, or exploratory analysis through coursework or work. - Interest in product analytics, business intelligence, learning analytics, or data storytelling. - Comfort with Python or R; familiarity with cloud platforms, data quality, governance, or analytics engineering is useful.

Benefits and work setup The 10-week internship is paid and remote, with a two-day in-person gathering in Brooklyn. It includes mentorship, hands-on training, product demonstrations, a speaker series, and collaboration with data, engineering, and product colleagues.