Lead Data Scientist

Job Summary


Responsibilities

• Build and deploy end-to-end ML systems for football intelligence products.
• Translate football concepts into scalable data models, metrics, and decision systems.
• Own the full ML lifecycle: experimentation, deployment, monitoring, and iteration.
• Work closely with Product, Engineering, and Computer Vision teams to solve real-world problems.
• Debug messy, imperfect data and design reliable analytical pipelines.
• Define technical direction for the data science function and raise engineering and analytical standards.
• Mentor junior team members and help hire and shape the future data science team.
• Drive prioritization and decision-making in ambiguous, fast-moving environments.
• Continuously learn and implement advanced research in sports analytics.

Requirements

• Strong ML fundamentals across supervised and unsupervised learning, experimentation, and model evaluation.
• Strong Python, SQL, data analysis, and a production engineering mindset.
• Experience taking ML systems from idea to production.
• Strong ownership, judgment, and decision-making ability.
• Ability to communicate clearly with both technical and business stakeholders.
• You must actively follow football and genuinely understand the game - formations, tactical systems, player roles, match flow, and performance context should feel natural to you.

Nice to Have

• Computer vision experience (YOLO, tracking, PyTorch, TensorFlow).
• Sports analytics experience.
• LLM or agentic AI experience.
• Public ML or football analytics work (GitHub, blogs, research).

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