Opportunities from Confidentialsportstradingfirm (greenhouse)

  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