Lead the design, development, and deployment of machine learning models for sports outcome prediction. This hands-on leadership role involves managing a team, owning the full model lifecycle, and driving betting strategy. Requires 5+ years of data science experience, with proven success in sports betting models and deep learning expertise.
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Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
About the Role
We're looking for a Lead Data Scientist to head our machine learning model development operation. You'll design, build, and deploy predictive models that estimate sports outcome probabilities, models that directly drive our betting strategy on prediction markets.
This is a hands-on leadership role. You'll own the full model lifecycle from research through production, lead a team of data scientists, and continuously improve our predictive edge.
The role will be fully remote with a generous compensation package.
Responsibilities- Lead the design, development, and deployment of machine learning models for sports outcome predictionÂ
- 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 back testing frameworks and rigorously validate model performance against historical data ​
- Collaborate with trading and engineering teams to integrate models into live betting operationsÂ
- Ensure data quality, pipeline integrity, and model monitoring in productionÂ
- Stay current with advances in sports analytics, ML research, and betting market dynamics ​
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- 5+ years of experience as a Data Scientist or ML Engineer, with at least 2 years in a leadership roleÂ
- Proven experience building sports betting or prediction models- this is essentialÂ
- Strong expertise in deep learning frameworks (PyTorch, TensorFlow) and techniques (neural networks, CNNs, Transformers) ​
- Advanced proficiency in Python and SQLÂ
- Solid foundation in statistics, probability theory, and predictive modellingÂ
- Experience deploying ML models to production environments
- Excellent communication skills- ability to translate complex findings for non-technical stakeholdersÂ
- Degree in Computer Science, Data Science, Statistics, Mathematics, Physics, or related quantitative fieldÂ
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