Referment
Quantitative Analyst – Execution Research (F2258D3)
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The Role
Referment is working with a sports research and technology business to find a Quantitative Analyst for its execution research team. The focus is on turning predictions into automated betting decisions: how a strategy should respond to prices, available liquidity and changing market conditions.
As a Quant Analyst you will research, develop and deploy automated market-making and betting strategies across a range of professional sports, covering pricing, liquidity, risk, order books and market behaviour in both pre-match and in-play markets. The role combines rigorous statistical modelling with production-quality engineering in a collaborative, research-led environment.
Responsibilities
- Develop and test execution strategies, investigating how predictive models can inform betting and market-making decisions.
- Frame clear research questions, analyse the evidence and explain what the results do and do not establish.
- Build probabilistic models and assess their performance, including the uncertainty around their predictions.
- Translate successful research into tested, documented code that fits shared analytical libraries.
- Review existing strategies with other researchers, identifying weaknesses and evaluating potential improvements.
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What We're Looking For
- A relevant master's qualification, a grounding in statistics and at least three years of relevant work experience; alternatively, a PhD or equivalent with relevant statistical training.
- Strong statistical and probabilistic modelling experience, with the judgement to challenge a result rather than accept it at face value.
- Confident programming in Python, R or a comparable language, extending beyond standalone analysis scripts to maintainable code.
- An organised, reproducible approach to research and the ability to communicate findings to colleagues with different technical backgrounds.
- A genuine interest in sports betting. Existing UK work permission is required; sponsorship is unavailable.
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Useful Background
- Experience in market making or automated trading is particularly relevant. Bayesian modelling, optimisation or machine learning can also be useful, depending on how you have applied them. This could suit a sports quant, a trading researcher or someone moving from advanced statistical research into applied execution work.
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