Quantitative Analyst
Smartodds
Quantitative Analyst
Smartodds
As a Quant Analyst, you will be responsible for providing clients with predictions for football outcomes across various betting markets. 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 performance provides 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.
Contribute to identifying promising research directions; ensuring research is carried out to the highest standard.
Extend existing predictive models for football and develop new ones, aligned with the needs of clients and the business.
Own and execute the relevant research projects, providing updates and estimates for delivery.
Contribute to discussions and efforts to identify weaknesses and potential improvements in existing models across all sports.
Contribute to the review of the work and code of others within the team.
Develop and maintain software that supports the delivery of our model predictions, and the mathematical libraries behind our range of tools, providing updates and estimates for delivery.
Attend at least one event a year to support your professional development as a sports quant analyst: conferences, courses, meet-ups, networking events; in-person or remotely; on sports, statistics, machine learning, gambling, etc.
Skills & Experience
Required
Technical
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,
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 and GLMs.
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.
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 pro-actively any blockers when needed.
Self-directed researcher who generates hypotheses worth testing and sees connections across domains.
Preferred
Demonstrated passion for working in sports modelling and sports analytics.: Evidenced by personal projects, MSc project in a related area, or statistical analyses of sports or teams.
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.
Explore more opportunities at Smartodds