Lead Data Scientist, Sports
Hybrid · Manchester, England, United Kingdom
Job Summary
Lead Data Scientist to spearhead development of advanced probabilistic models powering real-time betting markets. Develop models to determine odds and power in-play betting decisions using state-of-the-art machine learning techniques; collaborate across quantitative analysis, trading, and software teams; mentor junior colleagues; leverage Python/R and ML frameworks; deploy scalable models in cloud environments; drive data-driven insights aligned with business objectives.
Required Qualifications
- 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.
- 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.
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