Role Description:
Key Responsibilities
Squash Expertise & Rule Implementation
· Provide in-depth knowledge of squash rules , including let calls, strokes, and faults .
· Define edge cases and scenarios where AI-based refereeing could be ambiguous.
· Develop with engineers to translate game rules into system logic .
AI & Computer Vision Implementation/Testing
· Evaluate real-time ball tracking, player positioning, and contact detection .
· Validate AI-driven decision-making against real match footage.
· Work with computer vision engineers to fine-tune motion tracking models .
Data Analysis & Model Improvement
· Analyze match data and decision accuracy metrics (e.g., precision, recall).
· Suggest ways to enhance ball speed detection, player interactions, and referee inputs .
· Work with data engineers to refine ML models for squash gameplay understanding .
Collaboration & Feedback
· Provide continuous feedback to developers and product managers.
· Help design the user interface (UI) and user experience (UX) for referees.
· Assist in creating testing frameworks for AI referee training .
Qualifications:
· Bachelor’s degree in computer science, Engineering, or a related field.
· Experience as a professional player, coach, or referee is highly preferred.
· Technical expertise in sports engineering, AI, or computer vision applications.
· Hands-on experience with video analysis tools, motion tracking, or sports analytics .
· Ability to collaborate with AI/ML engineers and provide insights for system improvement.
Programming Skills:
· Experience with Python or other scripting languages.
· Knowledge of mathematical calculations, trajectory modeling, and decision logic.
· Familiarity with computer vision frameworks (OpenCV, TensorFlow, PyTorch).
· Understanding of object tracking, event detection, and motion analysis .
Nice-to-Have Skills
· Experience with real-time sports analytics or game-based software.
· Knowledge of AI-based decision-making tools in sports.
· Familiarity with data annotation tools and processes.
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