Sports Data Science Intern

Who is a Data Science Intern at SMT? 

Are you a sports fan? Do you have strong coding skills in Python or R? Are you ready to roll your sleeves up and work with real-world, messy data sets? If you answered yes, here’s your chance to kick off a career in sports technology with SMT as a Data Science Intern!

Our Data Science intern will collaborate with members of our Data Science and Core Solutions teams, and this team supports data analysis and applications developed for sports like hockey, baseball, tennis and horseracing. 

This opportunity requires exceptional attention to detail, curiosity and problem solving, as well as strong written communication and organization. 

What is Your Daily Impact at SMT?

As a Data Science Intern, you will line up with our Director of Data Science Solutions and Director of Core Solutions & Data Science to tackle data projects at a granular level, with data collected from player and object tracking and presenting findings to your team. 

Your key responsibilities include:

• Detect repeatable features and patterns in cleaned high-throughput data as inputs to more sophisticated models and measurements. 
• Design, build and evaluate descriptive and predictive numerical models to gain insights into player and team performance, development, and trends over time. 
• Prepare automated reports and data displays in consultation with our front-end engineering and design teams.

What Do You Bring to SMT? 

• Pursuing a Bachelor’s degree or higher in a data-science-focused program (e.g. statistics, applied mathematics, computer science, economics) or a bachelor’s degree in any subject with commensurate work experience. 
• Minimum 1 year experience with statistical computing software preferred (R with dplyr/tidyverse strongly preferred; Python, Julia, Matlab and similar are acceptable) – academic experience sufficient.
• Minimum 1 year experience in statistical and/or machine learning modelling is preferred, such as: generalized linear/additive/hierarchical models, mixture models, model averaging, Bayesian computation, bagging/boosting, random forest models, time series/survival models, clustering and classification – academic experience sufficient.
• Pursuing a Bachelor’s degree or higher in a data-science-focused program (e.g. statistics, applied mathematics, computer science, economics) or a bachelor’s degree in any subject with commensurate work experience. 
• Minimum 1 year experience with statistical computing software preferred (R with dplyr/tidyverse strongly preferred; Python, Julia, Matlab and similar are acceptable) – academic experience sufficient.
• Minimum 1 year experience in statistical and/or machine learning modelling is preferred, such as: generalized linear/additive/hierarchical models, mixture models, model averaging, Bayesian computation, bagging/boosting, random forest models, time series/survival models, clustering and classification – academic experience sufficient.

What Can SMT Offer You?

Your work will directly contribute to the success of our SMT teams, driving innovation and changing the game of sports broadcast, events and data collection technology.

Often described as feeling like a start-up with a 40-year track record of serving sports leagues and broadcast partners, this team offers the chance to collaborate with experts in their fields, grow within the company and be valued for your contributions. 

This internship is paid! 

Who is SMT?

SMT is the leader in real-time data and graphics solutions for the biggest events in sports. From the famous Yellow First Down Line to leading-edge analytics, virtual enhancements and behind-the-scenes tools like video board production and talent prompters, SMT powers the fan experience across every screen and stadium.

For over 40 years, we’ve helped shape the broadcast and in-venue presentation of the Olympics, Super Bowls, Triple Crowns, tennis Grand Slams, golf majors, NASCAR, NHL, NBA, NCAA tournaments and more.

But we’re more than our tech: Our team thrives on the adrenaline of live sports. We travel together, work hard together and form l