Summer Intern, Data Science Intern — LA Kings

For more than 20 years, AEG has played a pivotal role in transforming sports and live entertainment. Annually, we host more than 160 million guests, promote more than 10,000 shows and present more than 22,000 events around the world. We are committed to innovation, artistry, and community, and leverage the power of our 300+ venues, leading sports franchises, marquee music brands, integrated entertainment districts, premier ticketing platform and global sponsorship activations, to create memorable moments that give the world reason to cheer.

Our business is interwoven with the human mind and heart, and we strive to build a diverse and inclusive company that reflects the artists, athletes, and fans that we host; reach beyond traditional boundaries to support the communities in which we operate; and minimize our impact on the environment by adopting sustainable practices throughout our business operations.

If you want to be challenged to up your game and make a difference, then join us in giving the world reason to cheer!

Job Summary:

The Data Science intern will work within the Business Intelligence department and will collaborate with business operations teams to drive strategy, improve revenue, and drive efficiencies by identifying patterns, predicting outcomes, and providing actionable insights for our organization. To do this, they will collect, clean, model, and analyze data using machine learning, data engineering, and statistical analysis techniques.

Responsibilities:

Support team by creating data models that predict and recommend strategic sports decisions to create new revenue streams.
Mine through and analyze core business datasets which include: ticketing purchases, fan demographics, social media metrics, digital marketing spend, media valuations, tv ratings, web analytics, and app usage.
Understand and optimized existing models using statistical analysis and machine learning
Assist AEG sports entities by helping to move, store, and organize data in addition to sourcing data via web scraping and connecting to APIs
Gain experience applying data science principles to solve problems
Build data models that help solve high-impact business questions
Partner closely with the data science mentor to complete and present a final project

Qualifications:

To be eligible for this internship you must be a current student pursuing a Masters degree in Statistics, Applied Mathematics, Computer Science, Business Analytics, Data Science, Machine Learning, or related field.
Highly organized, resourceful, and dependable, with excellent interpersonal skills and oral and written communication skills
Passion for sports, live entertainment, data, and technology
Strong interest in data science and data engineering.
Experience with statistical analysis and machine learning using Python/R
Intermediate to advanced SQL knowledge
Experience with data visualization (PowerBI, Tableau, Matplotlib)
Experience with and/or interest in Large Language Models (LLM) or generative AI is a big plus
You have prior experience from internships, work, or projects in a related field
Excellent written and verbal communication skills
Ability to multi-task and work well under pressure
Desire to learn more about the sports and entertainment industry

Benefit:

Work on increasingly challenging and engaging real-world projects
Gain hands-on experience in event planning, marketing initiatives, and business administration
Collaborate with experienced sales and activation professionals
Work closely with experienced team members who coach and provide mentorship
Attend meetings, events, and other networking opportunities

Intern Perks:

Corporate networking
Resume review
Attend meetings, events, games, and other networking opportunities

Location:

El Segundo, California (On-site)

Pay Scale: $17.00-$19.00

AEG reserves the right to change or modify the employee’s job description whether orally or in writing, at any time during the employment relationship. AEG may require an employee to perform duties outside his/her normal description.