Quantitative Sports Internship
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Job Description
About the Company:
DL Trading is a company founded in 2020 by individuals with backgrounds in both financial trading and sports betting. Their team members initially worked in the sports gambling industry before transitioning to the financial sector, where they each built successful trading teams at prominent firms. DL Trading focuses on developing algorithms for trading in all major US sports.
Job Description: Quantitative Sports Internship – Summer 2025
This is a 10-week internship offering a hands-on experience in the trading industry, specifically within the context of algorithmic sports betting. Interns will collaborate with a team of quants and traders, receiving mentorship and feedback in a collaborative environment.
Responsibilities:
The internship provides exposure to various aspects of the business, including:
• Working on quantitative research projects.
• Developing object-oriented code.
• Contributing to production engineering tasks.
• Building and improving algorithmic predictive models for sports betting.
• Working across multiple sports and projects within the firm. The startup nature of the company requires a versatile approach, with interns expected to contribute to a variety of tasks.
Required Skills:
• Ability to thrive in a fast-paced, demanding environment and manage multiple responsibilities.
• Strong problem-solving skills and a desire to understand how systems work.
• Genuine interest in sports and sports analytics/sabermetrics (demonstrated experience is a plus).
• Proficiency in at least one of the following:
• Statistical modeling (particularly predictive modeling) using Python or R.
• Python development on a Linux platform.
• Building algorithmic trading models (in financial markets or sports betting).
• Experience with quantitative sports gambling or daily fantasy sports.
Preferred Skills:
• Prior internship or job experience in a trading, quantitative, or engineering role.
• Advanced coursework in statistics, optimization, operations research, and/or computer science.
