Data Science Intern
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Job Description
About Company and Job Description
Live Nation Entertainment is the world’s leading live entertainment company, comprised of global market leaders: Ticketmaster, Live Nation Concerts, and Live Nation Media & Sponsorship.
Ticketmaster is the global leader in event ticketing with over 500 million tickets sold annually and more than 12,000 clients worldwide.
Live Nation Concerts is the largest provider of live entertainment in the world promoting more than 40,000 shows and 100+ festivals annually for nearly 4,000 artists in over 40 countries.
Live Nation Media & Sponsorship creates strategic music marketing programs that connect over 1,000 brands with the 98 million fans that attend Live Nation Entertainment events each year.
Live Nation’s Concerts Division promotes a fun and upbeat work culture with opportunities for career and personal growth.
The Role:
Live Nation is seeking a Data Science Intern to join their Chicago-based team to support analytics, machine learning, and AI initiatives. The intern will gain hands-on experience manipulating and analyzing concert and venue data, building machine learning models, and leveraging data intelligence platforms.
What This Role Will Do:
• 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 including, but not limited to, regression, clustering, and optimization.
• Partner with engineering and IT teams to productionize models, debug/troubleshoot code, and integrate with other internal business systems.
What This Person Will Bring:
• Currently pursuing a Master’s degree in a relevant field, such as data science, computer science, statistics, or business analytics.
• Proficiency in Python.
• Proficiency in 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 (e.g., gradient-boosted machines, neural networks).
• Strong communication skills and the ability to summarize complex analyses.
• Experience with MLOps and model deployment pipelines is a plus, but not required.
