Lead Quantitative Analyst at SIS
London W1B 1NS, , United Kingdom -
Full Time


Start Date

Immediate

Expiry Date

10 Dec, 25

Salary

80000.0

Posted On

10 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Algorithms, Python, Mathematics, Machine Learning, Physics, Communication Skills, Platforms, Collaborative Environment, Statistics, Sports, R, Computer Science, Java, Code

Industry

Information Technology/IT

Description

LEAD QUANTITATIVE ANALYST – SPORTS & ESPORTS TRADING

Location: London (Hybrid – 3 days in the office each week)
Full-Time
Reporting to: Head of Sports Trading

SALARY £70,000 TO £80,000 DEPENDENT ON EXPERIENCE + EXCELLENT BENEFITS

Are you a data-driven innovator with a passion for sports and esports? Do you thrive in a fast-paced, collaborative environment where your models directly impact trading performance? If so, we want to hear from you!
We’re on the hunt for a Lead Quantitative Analyst to join our dynamic Trading Team. This is a high-impact role where you’ll be at the forefront of developing cutting-edge pricing models and algorithms that power our sports and esports betting platforms.

ESSENTIAL SKILLS & EXPERIENCE

  • Degree in Mathematics, Statistics, Computer Science, Physics, or Engineering
  • Substantial experience as a Quantitative Analyst in the sports betting industry
  • Proficiency in Python, R, Java, and/or C#
  • Strong knowledge of sports analytics and machine learning
  • Excellent problem-solving and communication skills
  • Ability to manage code across development environments
  • Experience in owning code from development to production
Responsibilities
  • Model Mastery: Build and refine sophisticated pricing models for sports and esports betting, driving high-margin opportunities.
  • Trading Support: Provide analytical insights to optimize trading strategies and performance.
  • Innovation Through Data: Integrate rich in-play data, customer insights, and live betting liabilities to evolve our pricing logic.
  • Machine Learning Magic: Apply ML techniques to enhance model accuracy and efficiency.
  • Cross-Team Collaboration: Work closely with developers, product managers, and analysts to bring your models to life.
  • Documentation & Communication: Ensure your work is clearly documented and effectively communicated across teams.
  • Mentorship & Development: Support the growth of junior analysts and continuously develop your own skillset.
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