Data Analyst

Full Time Edinburgh Posted on February 24, 2026 Closes:
March 6, 2026

Hibernian Football Club are hiring a Data Analyst on a full-time basis. The successful candidate will work on top of an existing database to design player evaluation models and develop analytical processes to support decisions within the recruitment department. They must be an efficient problem solver, with strong statistical thinking and a willingness to be across the latest research to keep us ahead of the game.

• Design statistical models to deepen our understanding of player evaluation.
• Identify and monitor potential targets through data scouting.
• Automate reporting processes and build dashboards to improve visibility of data across the recruitment department.
• Play a key role in squad planning – including ongoing analysis of current players through data analysis.
• Develop metrics aligned to our football philosophy to support decision making.
• Challenge current processes and practices to ensure department growth and innovation.
• Assist with the designation of projects and tasks to our Work Placement students.
• Undertake and attend appropriate CPD when required and available.
• Any other ad-hoc tasks as required of the role by Head of Recruitment, Director of Football or Senior Recruitment Staff.

• Valid PVG Check (this is an essential requirement & will be part of the ‘new starter’ process).
• BSc in Maths, Data Science or an equivalent degree.

• MSc in Maths, Data Science or an equivalent master’s degree.
• Industry recognised qualifications in Talent /Data Analysis (i.e. PFSA).

• Prior experience (including part-time and voluntary) within football, or evidence of open source work/technical articles relating to professional sports.
• Ability to interpret/contextualise and present insights from data.
• Confidence to have honest conversations and challenge colleagues.
• Highly motivated to work in football and recruitment.
• A practical knowledge of football and the role that data plays in the sport.
• Focused on self-development and growing your skill set in an elite environment.

• Proficiency in R or Python.
• Experience querying data with SQL.
• Knowledge of statistical methods, i.e. Bayesian inference, time series, causal inference, etc.
• Understanding of machine learning/AI fundamentals, including gradient boosting and neural networks.
• Commitment to data best practices, such as documentation, version control and reproducibility.