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Data Analytics Intern - Washington DC

Capitol Analytics Group
Washington DC
Estimated Salary
USD 22 – USD 28
Live Update
7 Mei 2026
Deadline
7 Mei 2027

Job Description

Join Capitol Analytics Group's dynamic data team and gain hands-on experience transforming government data into actionable insights. This 12-week paid internship offers mentorship from industry experts while working on real-world projects for federal agencies. You'll collaborate with senior data scientists to build predictive models and visualization dashboards that inform policy decisions. Our program includes weekly workshops on advanced analytics techniques and networking opportunities with DC's leading data professionals.

Responsibilities

  • Analyze complex datasets using SQL, Python, and R to identify trends and patterns
  • Create interactive data visualizations using Tableau and Power BI for stakeholder presentations
  • Assist in developing machine learning models for forecasting and classification tasks
  • Document analytical processes and maintain reproducible code repositories
  • Present findings to cross-functional teams through clear, data-driven narratives
  • Support ad-hoc analysis requests from federal agency partners
  • Participate in agile sprints and contribute to sprint planning and retrospectives

Qualifications

  • Currently pursuing a Bachelor's or Master's degree in Statistics, Computer Science, Economics, or related field
  • Proficiency in SQL and at least one programming language (Python/R preferred)
  • Experience with data visualization tools (Tableau, Power BI, or matplotlib)
  • Strong problem-solving skills and attention to detail
  • Ability to communicate complex technical concepts to non-technical audiences
  • Familiarity with statistical analysis methods and hypothesis testing
  • Authorized to work in the United States for the full internship period
  • Interest in applying data analysis to public policy or government initiatives

Required Skills

SQL Python R Tableau Power BI Data Visualization Statistical Analysis Machine Learning Data Mining

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