Quantitative Analyst – Execution Research (1F979CE)

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.

The Role

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.

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.

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.

#Referment