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Kody
Student
Milltown, Wisconsin
1
Question
Asked
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What does the road to getting into pro sports as a statistician or data scientist look like?
What are the chances of artificial intelligence completely eliminating the job that I want? What are things that I can do to build my resume that aren’t internships that require a good resume?
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1 answer
Jaime Arenas Leyva
Communication Analyst
5
Answers
Ciudad Obregón, Sonora, Mexico
Updated
Jaime’s Answer
Getting into pro sports as a statistician or data scientist is competitive but very possible with the right approach. The road typically involves a mix of strong technical skills, domain knowledge (sports-specific), and real-world experience—even if that experience isn’t from a formal internship.
Here’s what the path might look like:
Build strong foundations in statistics, machine learning, data wrangling, and visualization. Languages like Python and R are essential, and knowing SQL helps too.
Understand the sport you want to work in deeply. Teams want people who not only know data but also understand what the data means on the field, court, or ice.
Work on independent projects. These are incredibly valuable, especially if you don’t have internships yet. You can:
Analyze publicly available sports data (e.g., from Kaggle, FiveThirtyEight, or open APIs).
Build models (e.g., win prediction, player evaluation).
Write blog posts explaining your findings—communication matters!
Share your work on GitHub and LinkedIn to create a digital portfolio.
Get involved in online communities. Contributing to sports analytics forums or participating in competitions (like those run by the NBA Hackathon or NFL Big Data Bowl) shows initiative and builds visibility.
Network. Reach out to professionals in the field, attend conferences (even virtually), and don’t be afraid to ask for advice or informational interviews.
Here’s what the path might look like:
Build strong foundations in statistics, machine learning, data wrangling, and visualization. Languages like Python and R are essential, and knowing SQL helps too.
Understand the sport you want to work in deeply. Teams want people who not only know data but also understand what the data means on the field, court, or ice.
Work on independent projects. These are incredibly valuable, especially if you don’t have internships yet. You can:
Analyze publicly available sports data (e.g., from Kaggle, FiveThirtyEight, or open APIs).
Build models (e.g., win prediction, player evaluation).
Write blog posts explaining your findings—communication matters!
Share your work on GitHub and LinkedIn to create a digital portfolio.
Get involved in online communities. Contributing to sports analytics forums or participating in competitions (like those run by the NBA Hackathon or NFL Big Data Bowl) shows initiative and builds visibility.
Network. Reach out to professionals in the field, attend conferences (even virtually), and don’t be afraid to ask for advice or informational interviews.
Jaime, thank you!