Data Scientist – Sports Analytics and Performance Intelligence (NBA)

This is a remote position.<\/p>

We are looking for a Data Scientist to join a next generation sports analytics and performance intelligence platform supporting professional basketball organisations, including NBA teams. The role focuses on building machine learning models that transform large scale performance, tracking, and contextual data into actionable insights for coaching staff, analysts, medical teams, and front office stakeholders.<\/span><\/span> <\/span><\/span><\/b>The platform combines structured and real time sports data with machine learning and GenAI orchestration layers to support player evaluation, game strategy, workload optimisation, and injury risk management. It is already used in live analytical workflows and continues to evolve with deeper data integration and advanced reasoning components.<\/span>
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<\/span><\/span>Responsibilities<\/span><\/span><\/b>
<\/span><\/span><\/p>

• Develop machine learning models for player performance analysis and optimisation<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Analyse time series and event based sports data at scale<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Engineer features from tracking, workload, and contextual datasets<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Validate, monitor, and continuously improve models in production environments<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Collaborate with sports analysts and domain experts to refine analytical use cases<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Expose model outputs to analytical dashboards and downstream systems<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Integrate model outputs into LangChain and LangGraph based reasoning workflows<\/span><\/span><\/span>
  <\/span><\/span><\/span><\/li>
• Ensure model outputs are interpretable, reliable, and decision ready<\/span><\/span><\/span><\/span>
  <\/li> <\/ul>
  
  <\/div><\/span>
  

  Requirements<\/h3>

  • Strong hands on experience in Data Science and applied Machine Learning<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Proficiency in Python and core libraries such as Pandas, NumPy, and scikit\-learn<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Experience with time series modelling and feature engineering<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Solid SQL skills for analytical querying and data exploration<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Experience working with large scale or high frequency datasets<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Ability to translate domain questions into quantitative models<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Experience working in cross functional product teams<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li>
  • Fluent English for professional collaboration<\/span><\/span><\/span>
    <\/span><\/span><\/span><\/li> <\/ul>
    Nice to have<\/b><\/span>
    <\/span><\/span> <\/div>

    • Experience in sports analytics or performance modelling<\/span><\/span><\/span>
      <\/span><\/span><\/span><\/li>
    • Exposure to tracking data or event based datasets<\/span><\/span><\/span>
      <\/span><\/span><\/span><\/li>
    • Familiarity with LangChain and LangGraph for analytical orchestration<\/span><\/span><\/span>
      <\/span><\/span><\/span><\/li>
    • Basic experience with GenAI driven insight generation or narrative creation<\/span><\/span><\/span><\/span>
      <\/li> <\/ul>
      
      <\/div><\/span>
      

      Benefits<\/h3>

      • Solid, competitive salary<\/span><\/span><\/span>
        <\/span><\/span><\/span><\/li>
      • Work in a multinational environment on international projects<\/span><\/span><\/span>
        <\/span><\/span><\/span><\/li>
      • Comprehensive healthcare<\/span><\/span><\/span>
        <\/span><\/span><\/span><\/li>
      • Long\-term B2B contract with a stable project pipeline<\/span><\/span><\/span>
        <\/span><\/span><\/span><\/li>
      • Remote work model<\/span><\/span><\/span><\/span>
        <\/li> <\/ul>
        
        <\/div><\/span>
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