Data Scientist Intern – Sports Modeling

Job Description

Data Science Intern – Fall 2026 | hackajob

The Tone:
This is an internship at PrizePicks, located onsite in West Midtown Atlanta, GA. PrizePicks is the fastest-growing sports company in North America, operating as the leading platform for Daily Fantasy Sports across leagues like the NFL, NBA, and popular Esports titles. This role offers students a valuable opportunity to work directly with the data science team, contributing to model development and researching innovative approaches for player projection and line-setting in daily fantasy sports, which is central to the company’s core product.

The TL;DR
• Role: Internship
• Location: In-person, West Midtown Atlanta, GA
• Pay: $30 hourly
• Team: Data Science Team
• Mission: To contribute to the development of complex sports modeling and pricing solutions, enhancing projection and line-setting within daily fantasy sports.
• Tech Stack: Python, SQL

What You’ll Actually Do
• Learn: Engage directly with the PrizePicks data science team to understand their comprehensive approach to sports projection modeling, line calibration, and hold optimization across dozens of unique sports and diverse stat types.
• Develop: Actively contribute to the ongoing model development, iterative refinement, and strategic data collection processes specifically for player performance projections and dynamic pricing mechanisms.
• Research: Conduct in-depth research into novel and advanced approaches for tackling complex daily fantasy sports challenges, including precise calibration, sophisticated probabilistic forecasting, effective line movement strategies, and granular stat-level modeling.
• Optimize: Analyze and contribute to strategies aimed at optimizing the financial ‘hold’ across the platform, ensuring competitive and balanced game offerings.

The Must-Haves
• Background: Currently a student pursuing a degree in a strong technical or quantitative field such as computer science, statistics, mathematics, physics, or economics, demonstrating a foundational understanding of relevant principles.
• Experience: A demonstrable history of independent or academic projects specifically focused on sports analytics, probabilistic modeling, forecasting, or engaging with prediction markets.
• Skills: Strong, practical coding proficiency in Python for data analysis and modeling tasks, and expertise in SQL for data querying and management, coupled with a genuine interest in daily fantasy sports, prediction markets, or sports analytics.
• Bonus: Experience with lower-level programming languages (e.g., Rust, Java, C++); a background in advanced statistical methods such as calibration, Bayesian modeling, or the ability to build statistical models from the ground up; or documented success in open-source contributions, competitive data science platforms like Big Data Bowl or Kaggle, or academic research within sports analytics or forecasting.