Data Scientist, Market Making

About the role

From research and data collection through production deployment and monitoring, you will build and improve the predictive models and algorithmic decision systems that power automated pricing and trading. Working closely with algorithmic traders, quant engineers, and product teams, you will translate sports, market, and operational data into models that improve price quality, execution quality, market competitiveness, and risk management. This role requires flexibility around the sporting calendar, including occasional evenings, weekends, and holidays tied to major live events. In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

Responsibilities

• Model Development & Production: Develop, iterate, and deploy predictive models and algorithmic decision systems for automated pricing and market making
• Partner with algorithmic traders and quant engineers to test, deploy, and refine models and strategy logic in production
• Manage data preparation from collection through cleaning; build robust models with strong feature and model selection
• Pricing & Automation Research: Improve quoting, pricing, and exposure-management logic using sports data, market signals, and performance analysis
• Build backtesting, simulation, and monitoring tools to evaluate model calibration and risk-adjusted performance
• Construct models and analyses in response to questions from trading, product, and engineering stakeholders
• Performance Analysis & Monitoring: Create dashboards, diagnostics, and data visualizations for model health, execution quality, and communication
• Conduct post-event and post-trade analysis to identify opportunities to improve pricing and automation
• Evaluate model performance using historical results, live observations, and key metrics
• Trading Tools & Platform Collaboration: Work with engineering and product teams to improve trading tools, controls, and operational reliability
• Contribute to a production code environment; provide feedback on platform behavior and new feature validation
• Identify opportunities to use data and models to improve automation, pricing, and business results

Requirements

• Experience in data science, analytics, quantitative research, or engineering preferred
• Bachelor's degree in a highly numerate major