Lead Data Scientist, Sports

Job Description

As a Lead Data Scientist, you will spearhead the development of advanced probabilistic models that power real‑time betting markets.

The Quantitative Analysis department harnesses data‑driven insights to enhance our betting products and services. The team is responsible for designing, developing, and maintaining sophisticated mathematical models to provide accurate pricing across our sports betting products.

In this role, you will work with extensive datasets to develop models that determine odds and power in‑play betting decisions while leveraging state‑of‑the‑art machine learning techniques to analyse and interpret complex sports data. Our fast‑paced, delivery‑focused environment offers significant opportunities for technical growth and innovation.

This role is eligible for inclusion in the Company’s hybrid working policy.

• Ability to leverage in‑depth knowledge of sports and online gambling to develop relevant, impactful models aligned with business objectives.
• Master’s degree or PhD in Mathematics, Data Science, Computer Science, or a related quantitative field.
• Proven success in leading the design, development, and deployment of sophisticated predictive models.
• Expertise in Python/R and ML frameworks, such as scikit-learn, Tensor Flow, or PyTorch.
• Proven ability to design and implement complex machine learning solutions and guiding best practice.
• Deep understanding of statistical analysis and probability theory.
• Demonstrable experience overseeing the implementation of highly accurate, computationally efficient, and scalable models for large-scale production environments.
• Track record of mentoring junior colleagues and successfully leading technical projects.
• Experience with cloud computing environments for scalable solutions.

• Leading the development and implementation of innovative data solutions for complex challenges, solving problems without prescriptive approaches.
• Overseeing advanced analysis of large datasets to derive actionable insights aligned with business objectives.
• Developing predictive models and algorithms using statistical techniques and machine learning.
• Conducting and supervising rigorous statistical validation of models against historical and live data.
• Collaborating with trading teams to incorporate domain expertise into mathematical models.
• Partnering with software architects and developers to ensure alignment with technical solutions.
• Optimising model performance for both accuracy and computational efficiency.
• Researching and implementing novel approaches from academic literature and industry advancements.
• Mentoring less experienced team members, providing guidance, and conducting quality assurance to enhance overall team performance and capabilities.
• Identifying and defining new opportunities for data-driven insights.