Data Scientist II (Soccer)

• 3+ years of professional or research experience developing statistical or machine learning models in Python or R, with examples of work you personally owned from problem formulation and data preparation through validation and use
• Strong applied statistical and modeling judgment: select appropriate methods, explain their assumptions and trade-offs, and distinguish meaningful predictive signal from artifacts in the data
• Experience designing rigorous model evaluations, including preventing data leakage, choosing validation strategies appropriate to temporal or grouped data, quantifying uncertainty, and assessing performance across relevant player populations and contexts
• Experience building models with sports tracking data, including working with spatial and temporal relationships, noisy or incomplete observations, and data-quality challenges
• Ability to write maintainable, tested, and reproducible analytical code, using version control and code review to support collaborative development
• Proficiency in SQL to join, transform, and validate large, structured datasets used in model development
• Experience taking models beyond exploratory notebooks into repeatable workflows or production systems, collaborating with engineering and investigating issues as models are used
• Ability to explain modeling decisions, limitations, and results clearly to technical teammates and professional soccer clubs
• Strong data science fundamentals: understanding how models work, what they need from the data, and how to set data up to support them
• Strong communication skills — explaining modeling decisions directly to partner clubs, not just internal teammates

Soccer team (Data Science)

Primarily focuses on building models using both player tracking data and event data to identify undervalued players and characterize the skills that make them effective, supporting high-stakes recruitment decisions for professional clubs across global football.