Principal Quantitative Analyst - Sports Betting

Hard Rock Digital
Manchester
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

What are we building?

Hard Rock Digital is a team focused on becoming the best online sportsbook, casino, and social casino company in the world. We're building a team that resonates passion for learning, operating, and building new products and technologies for millions of consumers. We care about each customer's interaction, experience, behavior, and insight and strive to ensure we're always acting authentically.

Rooted in the kindred spirits of Hard Rock and the Seminole Tribe of Florida, the new Hard Rock Digital taps a brand known the world over as the leader in gaming, entertainment, and hospitality. We're taking that foundation of success and bringing it to the digital space — ready to join us?

What's the position?

We are looking for a Principal Quantitative Analyst specializing in Sports Betting to join our Quantitative Sports team. As a Principal Quantitative Analyst, you will lead the development and implementation of sophisticated sports simulations and mathematical models that drive our pricing strategies and risk management in the sports betting domain. This role requires an individual who's exceptionally skilled in quantitative analysis, statistical modeling, and has a deep understanding of sports betting industry.

Key Responsibilities:

  • Develop and maintain sophisticated sports simulation models to accurately price a wide range of sports betting markets
  • Lead the creation of proprietary algorithms for odds compilation and risk management across various sports and bet types
  • Collaborate with data engineering teams to ensure efficient processing and utilization of large-scale sports datasets
  • Implement and continuously improve models for live betting, taking into account real-time data and market movements
  • Conduct in-depth analysis of betting patterns and customer behavior to refine pricing strategies and identify potential risks
  • Work closely with trading teams to provide quantitative insights and support for decision-making
  • Stay abreast of the latest developments in sports betting technologies, incorporating new methodologies as appropriate
  • Mentor and guide junior quantitative analysts, fostering a culture of innovation and analytical rigor within the team


What are we looking for?

  • Extensive experience in developing and implementing complex sports simulation models for pricing and risk assessment in sports betting
  • Strong expertise in statistical analysis, machine learning, and predictive modeling techniques applied to sports.
  • Proficiency in programming languages such as Java, Go, C++, Rust, or Python for model development and data analysis
  • Deep understanding of probability theory, stochastic processes, and their applications in sports betting
  • Experience with big data technologies and distributed computing environments for processing large volumes of sports data
  • Ability to work with real-time data feeds and develop models for live betting scenarios
  • Strong problem-solving skills and meticulous attention to detail in analyzing sports statistics and trends
  • Excellent communication skills, with the ability to present complex quantitative concepts to both technical and non-technical stakeholders
  • Leadership experience in guiding and mentoring a team of quantitative analysts in the sports betting domain


Qualifications:

  • Ph.D. in Mathematics, Statistics, Physics, Computer Science, or a related quantitative field
  • Comprehensive knowledge of NFL and/or NBA, including team statistics, player performance metrics, and league-specific betting trends
  • 10+ years of experience in quantitative analysis, with a strong focus on sports modeling and sports betting
  • Proven track record of developing and implementing high-impact sports simulation models for pricing and risk management
  • Extensive experience with odds compilation and pricing strategies across various sports and bet types
  • Strong programming skills and experience with version control systems (e.g., Git)
  • Deep understanding of the sports betting market, including different bet types, market dynamics, and regulatory environment

Preferred:

  • Experience working with a major sportsbook or Quantitative sports company
  • Familiarity with regulatory requirements and compliance in the sports betting industry
  • Publications or patents related to sports modeling, quantitative modeling, or machine learning in betting contexts
  • Experience with real-time decision-making systems for live betting scenarios
  • Knowledge of esports and emerging betting markets

What’s in it for you?

We offer our employees more than just competitive compensation. Our team benefits include:

  • Competitive pay and benefits
  • Flexible vacation allowance
  • Flexible work from home or office hours
  • Startup culture backed by a secure, global brand

Roster of Uniques

We care deeply about every interaction our customers have with us and trust and empower our staff to own and drive their experience. Our vision for our business and customers is built on fostering a diverse and inclusive work environment where regardless of background or beliefs you feel able to be authentic and bring all your talent into play. We want to celebrate you being you (we are an equal opportunities employer)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.