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Internship Data Science Trading Jobs (NOW HIRING)

Athena LLC

Interns leave with a strong understanding of industry-standard data science practices and potential references for your career.

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Internship Data Science Trading information

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How much do internship data science trading jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for internship data science trading in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is the difference between Internship Data Science Trading vs Data Analyst?

AspectInternship Data Science TradingData Analyst
Required CredentialsRelevant coursework, basic programming skills, possibly some certificationsDegree in data-related field, analytical skills, proficiency in data tools
Work EnvironmentFinancial firms, trading floors, collaborative teamsVarious industries, offices, cross-functional teams
Employer & Industry UsageFinance, trading firms, investment banksMultiple sectors including finance, marketing, healthcare

Internship Data Science Trading focuses on applying data science skills to trading strategies within financial firms, often involving real-time data analysis. Data Analysts work across industries analyzing data to inform business decisions. While both roles require analytical skills and familiarity with data tools, internships are more training-oriented, whereas Data Analysts are more experienced roles in data interpretation and reporting.

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Internship Data Science Trading jobs near you
Infographic showing various Internship Data Science Trading job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Quantitative Trading Internship - 2027

Dime Line Trading

Chicago, IL

Internship

Posted 26 days ago

Job description

The internship at Dime Line is a 10-week opportunity with our team of quants, data scientists and traders, available all seasons of the year.

Dime Line Trading develops algorithms to trade all major US sports. Dime Line was founded in 2020 by veterans of the financial trading and sports betting industries, who started their careers in the sports gambling space before entering the financial industry, where they individually built successful trading teams at leading firms.

WHAT YOU'LL DO:
You will have the opportunity to work with our team while receiving feedback and mentorship from our tight-knit, collaborative employees in various roles. This is an opportunity to get your feet wet within the trading industry while utilizing algorithmic, statistical, and engineering concepts applied toward the sports betting industry. There are a number of different types of opportunities within Dime Line spanning quantitative research, algorithmic trading models, live trading, and data science. Interns will have the opportunity to work within multiple fields across multiple sports. We retain a startup culture, which means that interns will be working on production projects alongside the rest of the team.

SKILLS YOU'LL NEED:
Ability to work in a fast-paced environment and handle multiple demands at once
Interest in building solutions, solving problems, and understanding how things work
Interest in sports, sports analytics / sabermetrics - please let us know about your interest or work in sports analytics!

Hands-on experience and a high level of proficiency in one or more of the following:
- Statistical modeling, especially predictive modeling in Python/R
- Python development on Linux platform
- Building algorithmic trading models in financial markets, prediction markets or similar
- Quantitative sports gambling or daily fantasy sports

It's great to see:
- Past internship or job experience in a trading, quantitative or engineering role
- Advanced coursework in statistics, optimization, operations research, and computer science