Seatgeek
Data Analyst - Internship
New York, New York · Intern · Internship
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About the role
- SeatGeek believes live events are powerful experiences that unite humans.
- By catering to both consumers and businesses, we're powering a new, open entertainment ecosystem where fans have effortless access to experiences, and teams, venues, and shows have seamless access to their audiences.
- Because everyone should expect more from ticketing.
Responsibilities
- Develop expertise in core Analytics competencies, including exploratory data analysis, insight generation, reporting, KPI definition, and A/B testing
- Leverage first-class AI dev tools to build out measurement frameworks, develop key reporting, and collaborate with engineers to drive rapid iteration of features, bringing ideas to life faster than ever before
- Work with senior members of the Data Team to develop and execute business stakeholder roadmaps
- Evangelize data expertise, best practices, and documentation within the Data team and in collaboration with our business partners
- Strong collaboration skills and familiarity with using data points to build a narrative to guide business strategy and inform decisions
Requirements
- Over twelve weeks, you'll work closely with Business, Product, Design, and UX stakeholders, gaining experience with the fast-paced live events industry.
- You are a currently enrolled college student set to graduate in the spring/summer of 2028, ideally studying economics, psychology, computer science, statistics, mathematics, or another quantitative discipline
Compensation
- The compensation for this role is $45/hour USD (based on a 40 hour work week).
Benefits
- Monthly commuter card for transportation
- Housing stipend
- As an equal opportunities employer, we recognize that diversity is a positive attribute and we welcome the differences and benefits that a diverse culture brings.
Company info
- Make your mark We are hiring Data Analyst Interns to join us for summer 2027.
This listing is sourced directly from Seatgeek's careers page and normalized into a canonical job model.