Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Sports Quantitative Modeller

Merchant North
London
4 days ago
Create job alert

A Senior Sports Quantitative Modeller is a senior individual contributor and an expert in mathematical and statistical modeling, specifically for sports betting. This role is responsible for deriving new markets, understanding complex statistical distributions, and building robust, accurate quantitative models, particularly for BetBuilder products. They operate autonomously, owning the full lifecycle of their projects from conception to deployment, and providing technical guidance to junior modellers.

This role reports directly to the Data Science Manager and is an integral part of our Data Science - Core Modeling function.

Key Responsibilities
  • Design, develop, and implement advanced mathematical and statistical models for sports betting, with a primary focus on deriving new markets and enhancing existing offerings.
  • Possess a deep understanding of complex statistical distributions and leverage techniques such as Monte Carlo simulations in model development.
  • Rigorously backtest and validate models to ensure their robustness, accuracy, and profitability in real-world betting scenarios.
  • Drive and lead quantitative modeling initiatives, with a particular focus on BetBuilder products, from initial concept through to production deployment.
  • Operate with a high level of autonomy, owning and driving projects and solutions from conception to deployment, including managing own workload and project milestones.
  • Collaborate closely with Sports Trading, Product, and Engineering teams to ensure models are well-understood, seamlessly integrated, and align with engineering best practices and system architecture.
  • Provide technical guidance and mentorship to more junior quantitative modellers on modeling techniques, best practices, and project execution.
  • Proactively identify opportunities for advanced quantitative modeling to address business challenges and drive innovation within the sports betting domain.
  • Present complex quantitative findings and project outcomes clearly and persuasively to both technical and non-technical stakeholders, including senior leadership.
  • Create basic reports and visualisations using tools such as Tableau to communicate model performance and insights.
Job requirementsRequired Skills and Experience
  • Proven experience as a Quantitative Analyst/Modeller with a track record of successfully leading and delivering impactful quantitative models in a production environment.
  • Deep expertise in mathematical and statistical modeling, specifically applied to sports betting, including a strong understanding of complex statistical distributions and Monte Carlo simulations.
  • Highly proficient in Python for all modeling, analysis, and data manipulation work.
  • Strong experience in backtesting, validation, and performance evaluation of quantitative models.
  • Solid understanding of the end-to-end model development and deployment lifecycle in a production environment.
  • Excellent communication, interpersonal, and collaboration skills, with proven ability to work effectively with cross-functional teams (Trading, Product, Engineering) and manage stakeholder expectations.
  • Experience in deriving markets for various sports; experience with US sports is a valuable addition.
  • High attention to detail, precision in delivery, and strong problem-solving abilities.
  • Demonstrated ability to manage own workload and lead projects with a high degree of self-direction.
  • Experience with data visualisation libraries (e.g., matplotlib, seaborn, plotly) and creating basic reports in BI tools like Tableau.
Desirable Skills (Nice to Have)
  • General Machine Learning expertise.
  • Familiarity with big data concepts or platforms (e.g., PySpark, Hive) for data extraction and manipulation.
  • Experience with version control systems (e.g., Git) and MLOps principles.
  • Exposure to containerisation concepts (e.g., Docker) or job scheduling tools (e.g., Kubernetes).
Growth Path

This role represents a senior individual contributor path focused on deep quantitative expertise in sports modeling, with potential for even broader technical influence or specialisation within the quantitative domain.


#J-18808-Ljbffr

Related Jobs

View all jobs

Quantitative Analyst / Modeller - Sports Betting

Senior Data Engineering Manager

Senior Data Scientist (UK)

Executive Director / Principal Data Scientist

Staff Data Scientist (UK)

Quantitative Trader

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.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

Why the UK Could Be the World’s Next Data Science Jobs Hub

Data science is arguably the most transformative technological field of the 21st century. From powering artificial intelligence algorithms to enabling complex business decisions, data science is essential across sectors. As organisations leverage data more rapidly—from retailers predicting customer behaviour to health providers diagnosing conditions—demand for proficiency in data science continues to surge. The United Kingdom is particularly well-positioned to become a global data science jobs hub. With world-class universities, a strong tech sector, growing AI infrastructure, and supportive policy environments, the UK is poised for growth. This article delves into why the UK could emerge as a leading destination for data science careers, explores the job market’s current state, outlines future opportunities, highlights challenges, and charts what must happen to realise this vision.