Senior Football Data Scientist

About SkillCorner SkillCorner is a sports-technology company using artificial intelligence and computer vision to turn broadcast, tactical and wide-angle video into automated player and ball-tracking data. Our technology helps professional clubs and organisations make better-informed decisions across recruitment, scouting, performance analysis and football strategy. The Prediction Team combines tracking and event data to develop advanced game-intelligence and physical-performance metrics. We use machine learning and deep learning to understand player and team behaviour, and to turn complex football data into insights that support real-world decision-making. As a Senior Data Scientist, you will develop the models behind SkillCorner’s game-intelligence and physical-performance metrics. The problems are often challenging and open-ended, so you will need to make defensible modelling decisions, assess uncertainty and build solutions that can be trusted in real football environments. Key responsibilities • Lead modelling projects end to end , from problem definition and data exploration through development, evaluation, deployment and ongoing improvement. • Develop and improve machine-learning and deep-learning models for player evaluation, tactical analysis, predictive metrics and other applications involving player and team behaviour. • Work with high-volume, high-frequency and spatiotemporal data , addressing issues such as missing data, noise, bias and model reliability. • Navigate ambiguous problems, balancing research ambition with pragmatic solutions that meet genuine client needs. • Collaborate with data scientists, engineers, analysts, football specialists, business stakeholders and professional clubs to turn football questions and client requirements into robust solutions. • Help shape the team’s technical direction and standards, communicate modelling choices and limitations clearly to technical and non-technical audiences, and support less experienced colleagues. Preferred experience You should have: • Extensive professional experience applying data science, machine learning and deep learning to complex real-world problems, with strong Python skills for analysis, modelling and production-oriented development. • Direct experience working with football tracking and event data , or comparable high-volume, high-frequency and spatiotemporal datasets, with a clear understanding of their practical challenges. • Experience taking models from research into reliable production systems , including ownership of complete pipelines from data preparation and feature engineering through deployment, monitoring and maintenance. Attention to detail and familiarity with cloud providers and production infrastructure are expected. • A strong understanding of football and the ability to connect technical work to meaningful questions involving player roles, tactics, phases of play or team behaviour. • The ability to work effectively with ambiguity, make defensible modelling choices, assess uncertainty and balance technical ambition with pragmatic delivery. • Excellent communication skills in English , with the ability to explain complex analyses, modelling choices and limitations to technical colleagues, business stakeholders and professional football clubs. French would be an advantage. • A collaborative and proactive approach, with the ability to lead projects independently, make informed trade-offs, share ideas early and support less experienced colleagues. Nice to have: • Experience with football analytics or presenting analytical work to football clubs. Location: • Paris, France preferred, with regular visits to our Paris office. Remote work is possible within European time zones. Recruitment process 1. Introductory conversation A short call to discuss your background, motivations and the role. 2. Technical discussion A conversation about your experience with data science, machine learning, football data and production systems. 3. Practical case study and team meeting A technical exercise based on a realistic data science problem, presented to the Prediction Team. You will also meet members of the team and team management to discuss collaboration, communication and how you could contribute. 4. Final conversation A short conversation to validate the practical aspects of the offer. We will share the format and expectations for each stage in advance.