Data Science Intern
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Job Description
About the Company:
Live Nation Entertainment is the world’s leading live entertainment company, comprising Ticketmaster, Live Nation Concerts, and Live Nation Media & Sponsorship.
• Ticketmaster: The 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 focuses on tours, artists, and live music experiences for fans, fostering a fun and upbeat work culture with opportunities for growth. They offer various perks and benefits, aiming to create an inclusive and supportive environment for employees.
Job Description:
Live Nation is seeking a Data Science Intern to join their Chicago-based team and support analytics, machine learning, and AI initiatives.
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 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.
Qualifications:
• Currently pursuing a Master’s degree in a relevant field (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.
• Strong communication skills and the ability to summarize complex analyses.
• Experience with MLOps and model deployment pipelines is a plus, but not required.
