Lead Data Scientist

Job Summary

Build production-grade ML systems that shape how clubs, coaches, scouts, and players understand football at scale - and define the direction of our data science function.

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).

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.