We are looking for a Lead Sports Modeller to head our machine learning model development operations. The successful candidate will design, build, and deploy predictive models. They will also lead a team of data scientists and continuously refine our predictive edge.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Lead Sports Modeller – Prediction Models
Remote, London.
About the Role
I'm working with a quantitative trading firm looking to hire a Lead Data Scientist to head their machine learning model development operations. The successful candidate will design, build, and deploy predictive models that estimate sports outcome probabilities, directly informing strategic decisions in prediction markets.
This is a hands-on leadership role. The person will own the full model lifecycle from research through production, lead a team of data scientists, and continuously refine their predictive edge. The role is fully remote and offers a competitive compensation package.
Responsibilities
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- Lead the design, development, and deployment of machine learning models for sports outcome prediction.
- Manage and mentor a team of data scientists and data engineers.
- Build and validate deep learning architectures (CNNs, Transformers, neural networks) for structured sports data.
- Develop backtesting frameworks and rigorously validate model performance against historical data.
- Collaborate with trading and engineering teams to integrate models into live operations.
- Ensure data quality, pipeline integrity, and model monitoring in production.
- Stay up-to-date with advances in sports analytics, ML research, and prediction market dynamics.
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Requirements
- 5+ years of experience as a Data Scientist or ML Engineer, including at least 2 years in a leadership role.
- Proven experience building sports prediction or betting models (essential).
- Strong expertise in deep learning frameworks (PyTorch, TensorFlow) and techniques (neural networks, CNNs, Transformers).
- Advanced proficiency in Python and SQL.
- Solid foundation in statistics, probability theory, and predictive modeling.
- Experience deploying ML models to production environments.
- Excellent communication skills, able to explain complex findings to non-technical stakeholders.
- Degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or a related quantitative field.
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