Sports Betting Quantitative Researcher

An exciting opportunity to join a high-performance quantitative trading business developing predictive models and systematic strategies across sports betting markets.

• Job title: Sports Betting Quantitative Researcher
• Location: Central London / Hybrid
• Job Type: Full-time, Permanent
• Salary: £80,000+ base salary + profit-share bonus The Opportunity We are recruiting on behalf of a high-performance quantitative sports trading business looking for a Sports Betting Quantitative Researcher with proven experience building predictive models within sports betting. You'll develop and improve models and trading strategies across sports markets, working closely with experienced researchers, engineers, traders and the CIO. This is an end-to-end role where your models can move from research and backtesting into live trading. Key Responsibilities
• Build and improve predictive models for sports betting markets using TensorFlow/Keras, PyTorch or similar.
• Develop features from historical match data, player performance, Elo/rating systems, market odds and other sports data.
• Develop probability models and calibration techniques to improve predictive accuracy and market edge.
• Build trading signals, strategy parameters and position-sizing methodologies.
• Backtest models and strategies using realistic historical market prices, liquidity and time-aware validation.
• Identify and address overfitting, data leakage and look-ahead bias.
• Analyse live and post-trade performance to identify and improve sources of edge.
• Take ownership of models from initial research through to production and live trading.
• Present research, results and recommendations clearly to the CIO and trading team. About You
• 3+ years' experience building predictive models specifically within sports betting, sports trading or a closely related quantitative sports environment.
• Proven experience developing models that have been used to price or trade sports markets.
• Strong Python skills, including NumPy, pandas, SciPy and scikit-learn, plus solid SQL.
• Hands-on experience with machine learning and neural networks using TensorFlow/Keras or PyTorch.
• Strong understanding of probability, statistical modelling and model calibration.
• Practical understanding of betting markets, odds, overround, liquidity and closing line value.
• Knowledge of Kelly criterion, bankroll management and quantitative stake sizing.
• Strong research discipline, including walk-forward validation, avoiding leakage and controlling overfitting.
• Degree in Mathematics, Statistics, Physics, Computer Science or another quantitative discipline.
• Master's or PhD is advantageous but not essential.
• Strong communication skills and the ability to explain modelling decisions and research findings clearly. Desirable
• Experience modelling tennis, football, NBA or UFC.
• Experience with in-play or exchange betting.
• Experience with Elo, Glicko, TrueSkill or similar rating systems.
• Experience taking models from research into live production.
• Experience with Git, cloud infrastructure, model versioning and automated retraining.
• Demonstrable live betting results, model performance or published research. Why This Role? This is an opportunity for a sports betting quant to move beyond pure research and take genuine ownership of models that are deployed into live markets. You'll have direct access to senior decision-makers, see the performance of your models in real time and receive a profit-share bonus linked to the strategies you build and contribute to.