Quantitative Analyst – Club Football

About the role

As a member of the Quant Team, you will join an exciting environment predicting outcomes of professional sports on behalf of our clients. We focus on football, baseball, basketball, cricket, tennis, American football, ice hockey, horse racing and golf.

In this role, you will join our newly created Club Football team – a specialised sub-team within the Quant Team – developing statistical models exclusively for football. You will work closely with our partner clubs, including Brentford FC and Mérida AD, to improve performance through two core areas:

• In-game strategy optimisation
• Ratings players for the purpose of recruitment and development

This role combines rigorous statistical modelling with production engineering. You will take models from research through to deployment, writing well-tested, documented code that integrates into our internal libraries. The atmosphere is collaborative and academic – peer reviews, research talks, and further education opportunities – but unlike academia, the market and club performance provide immediate feedback on model quality. This makes the job challenging but also very exciting.

You will have plenty of autonomy to execute your models from idea to code to validation to (hopefully) deployment. However, this autonomy operates within a structured framework: established coding patterns, weekly check-ins, high test coverage, and strict reproducibility standards.

We highly value the personal development of our team members and you will therefore be able to allocate dedicated time to improve your skills and gain the necessary experience that will enable you to progress into more senior roles.

While we are open to applications from anyone who meets the minimum requirements, we would be especially keen to hear from applicants with substantial research experience and a demonstrable passion for football analytics.

Key Responsibilities

• Contribute to identifying promising research directions; ensure research is carried out to the highest standard
• Build models for in-game tactical optimisation
• Develop player recruitment models based on client needs, including player rating systems to quantify ability and performance
• Contribute to discussions and efforts to identify weaknesses and potential improvements in existing models across all sports
• Support club clients and internal stakeholders by developing, maintaining and supporting the mathematical libraries behind our range of tools and models, and software that delivers model predictions into production
• Perform statistical analysis of datasets, testing well-defined hypotheses and effectively communicating results to various stakeholders

Skills & Experiences

RequiredTechnical

• Either MSc in Statistics or a related field (e.g., Data Science or Mathematics) with 3+ years of relevant work experience (e.g. sports quantitative analyst for a club, betting syndicate, or bookmaker); or a PhD or equivalent in Statistics or a related field (e.g. Data Science, Mathematics) Candidates from adjacent fields (Computer Science, Engineering, Finance) are welcome via either route, provided they have solid applied statistics experience.
• Applied statistics: Solid understanding of GAMs, GLMs and ideally state-space models.
• Model diagnostics: Ability to evaluate predictive model performance using appropriate metrics, quantify and communicate predictive uncertainty
• Strong programming skills in a high-level language such as R (preferred) or Python. Must write production-quality code not just analysis scripts
• Clear written communication and documentation (including mathematical), can implement formulas from specs and code from formulas
• Demonstrated passion for working in sports modelling and sports analytics.: Evidenced by personal projects, MSc project in a related area, or statistical analytics of sports or teams

Working style

• Collaborative team player, with a constructive approach to receiving and applying feedback
• Understands the importance of reproducible research and properly tested code
• Thorough and reliable, with a verification mindset
• Ability to work autonomously while communicating proactively any blockers when needed
• Self-directed researcher who generates hypotheses worth testing and sees connections across domain

Others

• Ability to work in the UK

PreferredDomain:

• A strong interest in football demonstrated by previous attempts to model outcomes or analyse data
• Experience with player-level modelling
• Exposure to tracking data (physical metrics, positional data)
• Good understanding of sports betting markets

Technical

• Comfortable in R, in particular with data.table, mgcv and testthat for unit testing
• Engineering discipline: unit testing, config-driven pipelines, clean git workflow. Willing to write tests, document as you go, and follow strict code style
• Uses AI coding assistants as a development accelerator, not as a replacement for understanding. Writes precise instructions, reviews AI-generated diffs critically, and verifies behaviour before committing
• Experience with and/or knowledge of Bayesian models, state space models, filtering and smoothing, computational statistics and approximate inference methods
• Experience with and/or knowledge of machine and statistical learning, deep neural networks, feature engineering, reinforcement learning, dynamic optimisation and optimal control
• Experience with version control, code reviews and merge requests
• Experience with any additional programming languages (such as Python, C++ or Julia)
• Familiarity with database technologies, e.g., SQL, MongoDB, Redis, Postgres