Data Scientist at Derby County

Derby County are seeking to recruit a Data Scientist to join the Sporting Intelligence Department in a hybrid role, based at Moor Farm Training Centre. Working for Derby County Football Club offers a unique opportunity to be part of one of England’s most historic and passionately supported clubs. With a proud legacy, a dedicated fan base, and an exciting vision for the future, Derby County offers a dynamic and rewarding work environment both on and off the pitch. Whether you're joining the team in coaching, administration, marketing, or operations, you'll be contributing to a culture rooted in ambition, community, and progress. Employees benefit from a collaborative work environment and opportunities for professional development. The successful candidate will develop and apply statistical, machine‑learning, and predictive models to generate meaningful insight from complex football data, supporting the First Team and Academy. This role plays a central part in strengthening the club’s analytical capability by enabling evidence‑based decision‑making across football-specific areas such as scouting, recruitment, match analysis, sports science and medicine, and player development. Key Role Responsibilities: • Build and maintain statistical and ML models from event, tracking, and performance data. • Create core football metrics and performance models to guide and support decision‑making. • Develop spatio‑temporal models using event, tracking, and contextual data. • Build simulation and scenario models to support strategic and long‑term planning. • Apply Generative AI and Palantir AIP tools to enhance analytical workflows. • Contribute to the full modelling lifecycle from data preparation to deployment and monitoring. • Engineer model inputs aligned with the club’s ontology and data standards. • Maintain clear, reproducible code and documentation in shared code repositories. • Translate complex model outputs into clear, football‑relevant insights. • Develop lightweight tools or dashboards for self-service analytics. • Partner with key football stakeholders to deliver decision‑focused analytical solutions • Document modelling assumptions and monitor model performance for continuous improvement. • Ensure data governance, privacy, and responsible use compliance. • Stay up to date with football analytics research, modelling techniques, and emerging tools, applying new approaches where relevant. Essential personal characteristics and experience: • Experience developing, validating, and deploying statistical or machine‑learning models in real‑world applied settings • Advanced Python skills for writing clean, efficient, and maintainable analytical code • Experience with exploratory data analysis, feature engineering, and model evaluation techniques • Experience producing clear, structured, and reproducible code in shared repositories (Git‑based workflows) • Experience communicating analysis to technical and non‑technical stakeholders • Experience building lightweight dashboards or tools (e.g., Streamlit, Plotly, Foundry dashboards) • Ability to collaborate effectively with the Data and Analytics Lead and football departments, explaining technical decisions clearly • Ability to write high‑quality Python code that is reliable, tested, and suitable for integration into shared analytics platforms. • Ability to collaborate through code‑first workflows, shared repos, peer review, and version control • Strong understanding of supervised and unsupervised learning, regression, classification, time‑series analysis, cross‑validation, feature engineering, and model interpretability • Understanding of football principles, metrics, KPIs, and tactical concepts sufficient to contextualise analysis Desirable personal characteristics and experience: • Experience working specifically with football datasets (event, tracking, GPS, medical, recruitment) • Experience using football analytics libraries such as mplsoccer, SkillCorner tools, StatsBombPy • Experience building or optimising deep‑learning models using PyTorch, TensorFlow, or similar frameworks • Experience with spatial analytics, tracking data modelling, or simulation approaches in sport • Experience with Palantir Foundry or similar data platforms • Experience applying Generative AI tools such as LLMs to analytical workflows • Ability to frame football problems as analytical questions and identify appropriate modelling approaches Qualifications: Essential • Degree in a quantitative field (e.g., Data Science, Computer Science, Statistics, Mathematics, or related) Desirable • Postgraduate qualification in a relevant field is desirable but not essential Salary is competitive, dependent on experience. This is a full‑time role. Due to the demands of professional football, some weekend and evening work may be required. ...