The Data Analytics Intern will transform data into insights that support business decisions. During this 10-week program, the intern will complete a defined analytics project while collaborating with analysts, engineers, and business stakeholders.
This is a full-time, 10-week internship that will begin in early June 2027. This role will be hybrid (2x per week) from Reston, VA or may allow for flexible remote work from anywhere in the US. At this time, relocation or housing assistance is not available. Pay is expected to be $23/hour for undergraduate interns. Or $28/hour for graduate interns.
Key Responsibilities
- Gather and clarify business questions and reporting requirements.
- Query, clean, validate, and analyze data from enterprise systems.
- Identify trends, patterns, risks, and opportunities within datasets
- Develop dashboards, visualizations, or analytical reports.
- Define and document metrics, assumptions, and data sources.
- Communicate findings through concise narratives and presentations.
- Participate in team meetings, peer reviews, and stakeholder discussions.
- Present final insights and recommendations at the end of the internship.
Basic Qualifications
- A minimum of 30 college credit hours complete in data analytics, statistics, economics, business analytics, computer science, mathematics, information systems, or a related field.
- Due to contract requirements, U.S. Citizenship or Permanent Work Authorization is required
Preferred Qualifications
- A completed bachelor’s degree and enrollment verification or 9 credit hours completed in a master’s program in data analytics, statistics,
- economics, business analytics, computer science, mathematics, information systems, or related fields.
- Academic, project, or internship experience with data modeling, SQL and data analysis.
- Familiarity with a visualization tool such as Power BI, Tableau, or Looker.
- Strong critical-thinking, communication, and storytelling skills.
- Attention to detail and an interest in solving business problems with data.
- Ability to work effectively with both technical and nontechnical colleagues.
- Experience with Python, R, Excel, statistical analysis, or data visualization.
- Familiarity with data quality, KPI development, experimentation, or forecasting.
- Experience gained through coursework, research, case competitions, personal projects, or prior internships.
