Intern: Data Analytics / Data Science (Spring 2027)

At Rockstar Games, we create world-class entertainment experiences. Become part of a team working on some of the most rewarding, large-scale creative projects to be found in any entertainment medium - all within an inclusive, highly-motivated environment where you can learn and collaborate with some of the most talented people in the industry. Rockstar Leeds is on the lookout for students who have a passion for data analysis and the insights that data provide to help build an immersive game experience. This is a paid, fixed term contract position running for 15 weeks from late January 2027 to early May 2027, with the expectation of a 37.5-hour work week. The successful candidate will work in Rockstar’s unique game development studio in the heart of Leeds, England.

WHAT WE DO

The Rockstar Analytics team provides insights and actionable results to a wide variety of stakeholders across the organization in support of their decision making. We partner with multiple departments across the company, leveraging analytics to measure and improve on the success and health of our games. We collaborate as a global team to develop cutting-edge data pipelines, data products, data models, reports, analyses, and machine learning applications. We work alongside an exploding team of innovative data-driven decision makers directly impacting Rockstar’s future.

RESPONSIBILITIES

Provide strategic insight and predictive modeling on player behavior. Deliver reports and analysis projects using player, game, and platform data. Achieve well-scoped goals based on guidance and support from Leads and team members. Experiment with and explore new opportunities for machine learning applications while contributing to existing models & pipelines. Collaborate with fellow interns on Research and Development projects.

REQUIREMENTS

Please note this internship is intended for undergraduate students, postgraduate students, or recent graduates who have completed coursework in data science, data analytic