This is a unique opportunity to work at the cutting edge of AI, data technology, and elite football analytics. As a Data Scientist, you’ll join a collaborative, high-performing team with a culture rooted in creativity, innovation, and excellence.
In this role, you’ll design and deploy data models that power decision-making across every area of the club — from strategy, tactics, recruitment, and performance, to pre- and post-match analysis. You'll also play a key role in supporting the club’s commercial operations, including e-commerce and fan engagement.
If you're passionate about using advanced data science to drive real-world impact in sport, this is the role for you.
Key Responsibilities
- Develop and apply statistical models and algorithms to analyse player performance, match outcomes, and tactical insights
- Collect, clean, and process football-related data from diverse sources
- Build clear, compelling visualizations and deliver insights to both technical and non-technical stakeholders
- Collaborate with analysts, coaches, and performance staff to understand requirements and translate them into actionable data solutions
- Stay up to date with advancements in sports analytics, machine learning, and data science methodologies
Your Background
- 3+ years of industry experience as a Data Scientist, plus a strong academic foundation
- Python Data Science Stack: Advanced proficiency in Python, including Pandas, NumPy and scikit-learn.
- Statistical & Machine Learning Modelling: Experience with a variety of ML techniques (regression, classification, clustering, time-series forecasting)
- Experience with deep learning frameworks such as Keras or PyTorch
- Model Deployment: Proven ability to productionise models, including building and deploying APIs
- Strong visualization and communication skills, with the ability to translate complex technical findings into actionable insights for coaches, analysts, and execs
Highly Desirable Skills
- Football Analytics Experience: Familiarity with football-specific datasets (event, tracking, positional), and libraries like mplsoccer
- Advanced MLOps & Modelling: Experience with the Vertex AI ecosystem, especially pipelines, and advanced techniques such as player valuation, tactical modelling, etc.
- Bayesian Modelling: Knowledge of probabilistic programming (e.g., PyMC) for uncertainty-aware predictions
- Stakeholder Collaboration: Demonstrated ability to work directly with stakeholders to scope, iterate, and deliver impactful solutions in fast-moving environments
What They Offer
- A chance to work on real-world data that impacts elite football performance
- Access to high-value datasets, sports science teams, and cross-disciplinary experts
- A flexible hybrid working model (1 day per month in the London office)
- The opportunity to grow within a digital-first team at a world-renowned football club
- The satisfaction of applying your engineering skills in an environment where your work directly influences results on the pitch