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About the Role
Develop models for custom-built solutions for sports events and properties, contributing to broadcast, digital, and fan-facing products delivered via B2C/B2B applications and APIs. Collaborate within a cross-functional squad across various disciplines and technologies.
Responsibilities
- Develop, train, and evaluate models using statistical and machine learning techniques, focusing on probabilistic approaches.
- Contribute across the modelling lifecycle, including feature engineering, training, validation, and deployment.
- Query, clean, and explore datasets using Python and SQL to identify patterns and support model development.
- Assist in building and maintaining data pipelines for model dependencies, including ingesting and validating sports data.
- Leverage AI tools to enhance daily workflows.
- Provide go-live support for solutions built around specific sporting events.
- Apply model development discipline through version control, testing, and documentation.
Requirements
- Follow sport closely and understand the context of data and audiences.
- Possess a solid grounding in machine learning (supervised and unsupervised methods) and classical statistical techniques.
- Comfortable working with probabilistic models, uncertainty estimation, and Bayesian inference.
- Understand the full model training pipeline, including data preparation, feature selection, model selection, and model validation.
- Hands-on experience building and evaluating models in a data science or quantitative context.
- Proficient in using Python and SQL for data exploration, feature development, and modeling workflows.
- Interest in the engineering aspects of data pipelines and ownership.
- Maintain a client-centric approach and communicate findings clearly to technical and non-technical audiences.
- Ability to explain work directly to client stakeholders and translate needs into modeling decisions.
- Naturally curious about the "Why" and use data and user behavior to inform decisions.
- Collaborative and open to contributing to shared knowledge and code bases.
- Work well within a cross-functional team.
- Keen to develop knowledge and skills, staying updated with relevant developments.
Skills
- Machine Learning
- Statistical Techniques
- Probabilistic Models
- Uncertainty Estimation
- Bayesian Inference
- Model Development Lifecycle
- Data Preparation
- Feature Selection
- Model Selection
- Model Validation
- Python
- SQL
- Data Exploration
- Data Engineering
- Client Communication
- AI-assisted Development
- Version Control
- Testing
- Documentation
- Golf (preferred)
- AWS (Lambda, EventBridge, DynamoDB) (preferred)
- Monte Carlo Methods (preferred)
- Probabilistic Simulation (preferred)
- AI-assisted Coding Tools (Claude Code, Cursor) (preferred)
Location
- London
Work Type
- Hybrid working
Experience Level
- Hands-on experience building and evaluating models
Salary/Compensations
- Salary based on external benchmarking framework, plus eligibility for a bonus scheme
Benefits
- Private health insurance
- Personal days, including birthdays and health and wellness days
About the Company
- AI forward culture