Responsibilities
- Develop machine learning models for player performance analysis and optimisation
- Analyse time series and event based sports data at scale
- Engineer features from tracking, workload, and contextual datasets
- Validate, monitor, and continuously improve models in production environments
- Collaborate with sports analysts and domain experts to refine analytical use cases
- Expose model outputs to analytical dashboards and downstream systems
- Integrate model outputs into LangChain and LangGraph based reasoning workflows
- Ensure model outputs are interpretable, reliable, and decision ready
Basic qualifications
- Strong hands on experience in Data Science and applied Machine Learning
- Proficiency in Python and core libraries such as Pandas, NumPy, and scikit-learn
- Experience with time series modelling and feature engineering
- Solid SQL skills for analytical querying and data exploration
- Experience working with large scale or high frequency datasets
- Ability to translate domain questions into quantitative models
- Experience working in cross functional product teams
- Fluent English for professional collaboration
Preferred qualifications
- Experience in sports analytics or performance modelling
- Exposure to tracking data or event based datasets
- Familiarity with LangChain and LangGraph for analytical orchestration
- Basic experience with GenAI driven insight generation or narrative creation
Benefits
- Solid, competitive salary
- Work in a multinational environment on international projects
- Comprehensive healthcare
- Long-term B2B contract with a stable project pipeline
- Remote work model
Tags & focus areas
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