Data Scientist - Risk & Trading (Daily Fantasy Sports)

July 26

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Sleeper

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Fantasy leagues with friends

Mobile Application Development • Social Enterprise • Fantasy Sports • Mobile Messaging

11 - 50

Description

• Support risk and liability management with respect to Sleeper’s DFS operations, conducting data analysis and modeling to identify trends, patterns, and insights. • Develop predictive models and algorithms to better understand Sleeper’s real-time liability, including risk exposure related to specific markets and events. • Conduct data wrangling, processing and cleaning operations, and design processes, as well as tools for monitoring and analyzing performance and accuracy. • Monitor all pricing for real-time odds, active player statistics markets, breaking news, and other relevant information to make real-time market adjustments / suspensions, while also ensuring that data flows from external partners are being recorded timely and accurately. • Build out user risk profiles, staying close to behavior patterns and trends, and collaborate with product and engineering teams to enhance personalization within our DFS ecosystem. • Automate risk and trading processes to scale for growth in users, sports, and markets in support of all game operations, and contribute to research and innovation for this function as a whole.

Requirements

• Background in computer science, data science, machine learning, mathematics/statistics, or a similar field with related experience. Comfortable working with and analyzing large data sets. • In-depth knowledge of DFS (Daily Fantasy Sports), sports betting, player props, handicapping, expected value, and closing line value. Ideal candidates have extensive experience with high volumes of DFS/sports betting and real money games, either as a trader, +EV bettor, and/or oddsmaker. • Proactive – ready to hit the ground running; excited about the idea of building out processes from scratch, and not afraid to break the “sports trading mold”, challenging how the industry has historically approached risk management and trading. • Comfortable working “off hours” to align with peaks in the sports calendar. This will often include nights and weekends. We are generally quite flexible and will work with you to ensure an appropriate work/life balance is molded around game schedules. • Knowledge of SQL, Python, and/or related software/tools, and familiarity in working with databases, data warehouses, data visualizations and report building. • Experience with player performance metrics, game outcome predictions, in-game event modeling, as well as working with real-time sports data feeds and APIs.

Benefits

• Medical • Dental • PTO • 401k