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Smartodds

Quantitative Analyst - Football

Quantitative ResearchConfirmed live in the last 24 hours
Full-timeLondon, UKIn-person

About the Job

Requirements

  • An MSc in Statistics or a related field, such as Data Science or Mathematics, plus at least 3 years of relevant work experience; alternatively, a PhD or equivalent in Statistics or a related field. Candidates from adjacent fields including Computer Science, Engineering, or Finance may qualify through either route if they have solid applied statistics experience.
  • A solid understanding of generalized additive models and generalized linear models.
  • Ability to evaluate predictive model performance using appropriate metrics and quantify and communicate predictive uncertainty.
  • Strong programming skills in a high-level language such as R or Python, including the ability to write production-quality code rather than only analysis scripts.
  • Clear written communication and documentation, including mathematical documentation, and the ability to implement formulas from specifications and code from formulas.
  • Ability to work in the UK.

Responsibilities

  • Contribute to identifying promising research directions and ensure research is carried out to a high standard.
  • Extend existing football predictive models and develop new models aligned with client and business needs.
  • Own and execute relevant research projects, providing updates and delivery estimates.
  • Contribute to discussions and efforts to identify weaknesses and potential improvements in existing models across all sports.
  • Contribute to reviewing the work and code of others on the team.
  • Develop and maintain software supporting delivery of model predictions and the mathematical libraries behind the tools, providing updates and delivery estimates.
  • Attend at least one professional-development event each year, such as a conference, course, meet-up, or networking event, in person or remotely, on topics such as sports, statistics, machine learning, or gambling.

Desired Qualifications

  • Demonstrated passion for sports modelling and sports analytics, evidenced by personal projects, a related MSc project, or statistical analyses of sports or teams.
  • Comfort with R, particularly data.table, mgcv, and testthat for unit testing.
  • Engineering discipline, including unit testing, config-driven pipelines, and a clean git workflow; willingness to write tests, document work as it progresses, and follow strict code style.
  • Use of AI coding assistants as a development accelerator, with precise instructions, critical review of generated diffs, and verification of behavior before committing.
  • Experience with or knowledge of Bayesian models, state space models, filtering and smoothing, computational statistics, and approximate inference methods.
  • Experience with 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 additional programming languages such as Python, C++, or Julia.
  • Familiarity with database technologies such as SQL, MongoDB, Redis, and Postgres.

About the Company

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