Lead Data Scientist, Sports

We’re one of the world’s leading online gambling companies, revolutionising the industry since 2000.

Founded by Denise Coates CBE, we now employ over 10,000 people and serve over 120 million customers in 26 languages.

We empower our employees to push boundaries and explore new ideas, cultivating a culture that celebrates and rewards creativity.

This offers employees a wealth of growth opportunities, giving them the opportunity to make a real impact in the world of online gambling.

As a forward-thinking company, we’re breaking new ground in software innovation too, redefining what’s possible for our global worldwide.

Our focus on In-Play betting has solidified our market-leading position, featuring more than 1.38 million In-Play sporting events a year.

With over 750 concurrent sporting fixtures at peak and more live sports streamed than anyone else in Europe (750,000), we handle over 6 million HTTP requests daily and process more than 1.5 million bets per hour at peak.

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 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, TensorFlow, or PyTorch,
• Proven ability to design and implement complex machine learning solutions and guiding best practice.