Data Scientist - Technology Solutions

Role Overview We’re looking for a Data Scientist to join our Technology Solutions squad. You’ll develop the models behind our custom-built solutions for sports events and properties, contributing to a growing portfolio of broadcast, digital and fan-facing products delivered via B2C/B2B applications and APIs. You’ll sit within a cross-functional squad and work closely with colleagues across the business, so you’ll need to be comfortable collaborating across disciplines and different technologies. What You’ll Do Modelling & Analysis: Develop, train and evaluate models using statistical and machine learning techniques, with a focus on probabilistic approaches. Contribute across the modelling lifecycle from feature engineering and training through to validation and deployment.Data Work: Query, clean and explore datasets using Python and SQL to surface patterns and support model development.Data Pipelines: Help build and maintain the pipelines your models depend on, ingesting and validating new and often messy sports data sources.AI-Assisted Development: Leverage AI tools to accelerate and improve your day-to-day workflow.Event Support: Our solutions are often built around specific sporting events, meaning fixed deadlines and go-live support requirements that you will help to provide.Quality & Rigour: Apply good model development discipline through version control, testing and documentation.Requirements What You’ll Bring Passion for Sport: You follow sport closely and understand the context of the data and audiences we build for. Comfortable with sport-driven modelling decisions.Machine Learning & Statistics: Solid grounding in machine learning, supervised and unsupervised methods, and classical statistical techniques. Comfortable working with probabilistic models, uncertainty estimation and Bayesian inference.Model Development: Understanding of the full model training pipeline, including data preparation, feature selection, model selection and model validation.Experience: Hands-on experience building and evaluating models in a data science or quantitative context.Python & SQL: Comfortable using Python and SQL for data exploration, feature development and modelling workflows.Interest in Data Engineering: An appetite for the engineering side of the work — you want to understand and help own the pipeline that feeds your model, rather than hand that problem to someone else.Client-Centricity & Communication: You keep the client in mind throughout, and can present findings clearly to both technical and non-technical audiences. You’re comfortable explaining your work directly to client stakeholders and translating what they need into modelling decisions.Nice to Haves Golf: A passion for golf is a real advantage. A significant share of this squad’s work is golf, so familiarity with strokes gained, shot-level data and how a tournament unfolds will let you contribute quickly.Data Engineering: Experience building and maintaining production data pipelines, or working with AWS services such as Lambda, EventBridge and DynamoDB.Simulation: Experience with Monte Carlo methods or probabilistic simulation.AI Integration: Comfortable using AI-assisted coding tools such as Claude Code or Cursor as part of your everyday workflow, and open to integrating them deeper into how you model and build.What We Look For Curiosity: You are naturally curious about the “Why”. You look at data and user behaviour to inform your decisions.Collaborative & Open: You treat your work as a starting point for collaboration. You contribute to shared knowledge and code bases, and work well within a cross-functional team that brings together colleagues from across the business.Client-Centric: You care about the people using what you build. You listen to what clients and stakeholders actually need and let that shape how you approach a problem.Continuous Development: You are keen to develop knowledge and skills, keeping up to date with relevant developments and applying new learning where appropriate.Benefits What We OfferHybrid working out of our London office (Farringdon) - most of our staff come into the office about three days a weekSalary based on our external benchmarking framework, plus eligibility for a bonus schemePrivate health insurancePersonal days, including birthdays and health and wellness daysAI forward culture