Data Science Intern
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Job Description
About the Company:
Live Nation Entertainment is the world’s leading live entertainment company, comprised of Ticketmaster, Live Nation Concerts, and Live Nation Media & Sponsorship.
• Ticketmaster: A global leader in event ticketing.
• Live Nation Concerts: The largest provider of live entertainment, promoting numerous shows and festivals annually.
• Live Nation Media & Sponsorship: Creates strategic music marketing programs connecting brands with fans.
Live Nation’s Concerts Division fosters a fun and upbeat work culture with perks like free concert tickets, dog-friendly offices, and progressive benefits. They are certified as a Great Place to Work and one of People Magazine’s “50 Companies that Care”.
Job Description:
Live Nation is seeking a Data Science Intern to join their Chicago-based team. This intern will support analytics, machine learning, and AI initiatives, gaining hands-on experience with concert and venue data.
Responsibilities:
• Partner with Data Scientists to design, implement, and maintain retrieval-augmented generation (RAG) pipelines and AI applications powered by large language models (LLMs), including vector embedding store setup, prompt engineering, and integration with internal business systems.
• Conceptualize, build, and implement Machine Learning models within the Databricks platform.
• Build complex SQL queries to compile, manipulate, and aggregate data.
• Perform ad-hoc statistical/exploratory analysis.
• Partner with engineering and IT teams to productionize models, debug/troubleshoot code, and integrate with other internal business systems.
Requirements:
• Currently pursuing a Master’s degree in a relevant field (data science, computer science, statistics, or business analytics).
• Proficiency in Python and SQL.
• Experience working with LLMs, including prompt-engineering and RAG pipelines.
• Experience using statistical modeling and machine learning to solve business problems.
• Experience working with large and complex datasets.
• Thorough understanding of machine learning principles and experience using machine learning algorithms.
• Strong communication skills.
• Experience with MLOps and model deployment pipelines is a plus.
