Quant Analyst

Build predictive models that directly influence real trading decisions with a profitable prop trading team.

You'll design and deploy machine learning models to forecast race outcomes and identify market inefficiencies. Your research translates into profitable trading strategies, working closely with experienced traders to validate insights and optimise performance.

No client reporting or endless stakeholder meetings here. They trade entirely with their own capital, so your focus stays on finding edge and generating profit. You'll work with Python, scikit-learn, XGBoost, and PyTorch on cloud platforms, processing racing data, market pricing feeds, and betting exchange information. The team values rigorous backtesting and statistical validation before any model goes live.

The leadership team brings serious expertise. The Director has 20+ years building successful trading operations and developing top quantitative talent. Multiple team members joined specifically to work alongside him. They've been profitable every year since 2021 and grown to 40 staff across trading, research, and engineering functions. You'll collaborate directly with traders who understand the commercial reality of your models.

What You'll Do

• Design time-series forecasting and classification models to predict race outcomes and market movements
• Research market inefficiencies by exploring large datasets and testing statistical hypotheses
• Conduct rigorous backtesting to validate model performance before deployment
• Work with traders to translate research insights into profitable trading strategies

What You'll Need

• Strong background in mathematics, statistics, physics, engineering, or data science
• Experience building predictive models using Python, pandas, NumPy, and machine learning libraries
• Quantitative research experience with statistical analysis and hypothesis testing
• Track record of deploying models that generated measurable business impact

About the Company

They're a proprietary trading and wagering business using quantitative research and machine learning to identify profitable opportunities across global markets. Team of 40 staff, profitable since 2021, focused purely on finding edge and generating returns.

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