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.

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

Required Technical:

• Either MSc in Statistics or a related field (e.g., Data Science or Mathematics) with 3+ years of relevant work experience; or a PhD or equivalent. 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.

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

Preferred Domain:

• 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

Preferred 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
• Uses AI coding assistants as a development accelerator, not as a replacement for understanding
• 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

Benefits

From Day One:

• 30 days holiday (in addition to bank & public holidays)
• In-house chef*
• In-house masseuse*
• Team sporting events
• 25% discount on Brentford Football Club merchandise
• Cycle to work scheme
• Employee Assistance Programme
• Interest-free travel season ticket loan
• Offsite trips

*Available on selected days

After 3 Months:

• Pension – Employer Contribution starting at 5.5%, and employee starting at 2.5%
• Income protection – 75% of salary (subject to terms & conditions)

After Probation:

• Dedicated time for professional development – conferences, courses, meet-ups, networking events (at least one annually)
• Private Medical Insurance – including coverage of any excess payment
• Health Cash Plan via Medicash
• Life Assurance (4 x times earnings at time of death)
• Enhanced Company Sick Pay
• A discretionary annual bonus

After 2 Years:

• Increase in employer pension contribution to 6% (with a minimum employee contribution of 3%)
• Enhanced Maternity Pay
• Enhanced Paternity Pay

After 4 Years:

• Increase in employer pension contribution to 7% (with a minimum employee contribution of 3.5%)

About [Employer hidden]

[Employer hidden] is a privately-owned company providing statistical research and sports modelling services to customers, including professional gamblers and professional football teams. Our employees are based in North London with a number of consultants based globally. Founded in 2004, [Employer hidden] has a proven record of providing exceptional services to its clients. We are a growing company and work in an exciting and fast-paced industry of which we are a market leader. [Employer hidden] achievements are built around the talents of its employees.

Contact: [contact hidden]

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