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White Swan Data

Data-driven betting models; liquidity provider

Trader

Quantitative Trading / Algorithm DevelopmentUpdated on Nov 13
Full-timeNew York, NY, USANo salary listedIn-person

About the Job

Requirements

  • A BSc in Mathematics or a similar discipline, such as Physics, Economics, or Engineering.
  • A basic understanding of betting processes and betting markets.
  • Availability to work evenings, weekends, early mornings, and late nights.
  • Ability to act precisely on instructions and communicate technical issues clearly.
  • Ability to balance multiple tasks and manage time proficiently.
  • A proactive approach and desire to succeed.

Responsibilities

  • Manage the trading system to maximize trade volume and meet target metrics.
  • Work closely with developers and quants to improve the trading system by providing practical feedback.
  • Work closely with the operational support team to optimize the trading pipeline.
  • Solve practical problems quickly to minimize trading downtime.
  • Assist the administration team in achieving its targets.

Desired Qualifications

  • A good understanding of betting and odds.

About the Company

White Swan Data

Data-driven betting models; liquidity provider

White Swan Data is a London-based firm that builds data-driven betting models to identify opportunities in iGaming and prediction markets. It provides mathematics and data-led services to price risk and develop betting strategies, and acts as a market maker and liquidity provider for prediction exchanges and related platforms. It quotes prices, absorbs event risk, and supplies liquidity to deepen markets, sometimes handling a large share of activity on secondary exchanges. Its team of mathematicians, data scientists, and software engineers builds the infrastructure to support gambling and trading services, aiming to deliver reliable liquidity and scalable trading systems for clients.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2019

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