Job Description
As a Data Scientist, you will develop 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. 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.
Qualifications
- Bachelor’s degree in Mathematics, Data Science, Computer Science, or a related quantitative field.
- Keen interest in a wide range of sports and/or the online gambling industry.
- Demonstrable experience building predictive models from data.
- Expertise in statistical analysis and probability theory applied to real‑world problems.
- Strong Python/R skills with experience in ML frameworks, such as scikit‑learn, TensorFlow, or PyTorch.
- Ability to optimize models for both accuracy and computational efficiency.
- Experience handling and analysing large datasets.
- Familiarity with cloud computing environments.
Additional Information
- Conducting in‑depth analysis of large datasets to extract insights and inform decision‑making in sports betting.
- Utilising statistical techniques and machine learning algorithms to develop predictive models and algorithms for sports betting.
- Devising innovative solutions for unstructured problems.
- Assessing existing models and algorithms to identify areas for improvement.
- Identifying and resolving data quality issues or bugs affecting models or analysis.
- Performing rigorous statistical validation of models against historical and live data.
- Collaborating with trading teams to incorporate domain expertise into mathematical models.
- Optimising model performance for both accuracy and computational efficiency.
- Researching novel approaches from academic literature and industry developments.
- Identifying and defining new opportunities for data‑driven insights.
Job Description
As a Data Scientist, you will develop 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. 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.
Qualifications
- Bachelor’s degree in Mathematics, Data Science, Computer Science, or a related quantitative field.
- Keen interest in a wide range of sports and/or the online gambling industry.
- Demonstrable experience building predictive models from data.
- Expertise in statistical analysis and probability theory applied to real‑world problems.
- Strong Python/R skills with experience in ML frameworks, such as scikit‑learn, TensorFlow, or PyTorch.
- Ability to optimize models for both accuracy and computational efficiency.
- Experience handling and analysing large datasets.
- Familiarity with cloud computing environments.
Additional Information
- Conducting in‑depth analysis of large datasets to extract insights and inform decision‑making in sports betting.
- Utilising statistical techniques and machine learning algorithms to develop predictive models and algorithms for sports betting.
- Devising innovative solutions for unstructured problems.
- Assessing existing models and algorithms to identify areas for improvement.
- Identifying and resolving data quality issues or bugs affecting models or analysis.
- Performing rigorous statistical validation of models against historical and live data.
- Collaborating with trading teams to incorporate domain expertise into mathematical models.
- Optimising model performance for both accuracy and computational efficiency.
- Researching novel approaches from academic literature and industry developments.
- Identifying and defining new opportunities for data‑driven insights.
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