Data Scientist

Role: Data Scientist Work location (City): Osterley, UK Duration of the contract: 3 to 6 Months (Extendable) Hybrid work model: Min 4 days from client location Experience Range: 12+ years What you’ll do: • Lead the end‑to‑end development of AI solutions using Computer Vision, Machine Learning, Generative AI, and data science to enable capabilities such as automated sports metadata generation and detection of key events in live content and data streams. • Generate actionable insights for player performance, contextual statistics, and injury risk by designing models with embedded responsible and ethical AI principles from design through deployment. • Integrate model‑driven insights into personalisation engines, tailoring recommendations based on favourite teams, players, match context, and other signals while ensuring transparency, fairness, and appropriate use of data. • Define advanced experimental designs, lead A/B testing, develop and maintain metrics and dashboards, establish robust MLOps practices, and own end‑to‑end productionisation from data ingestion through deployment and ongoing model monitoring. • Design, architect, and operate low‑latency, highly reliable cloud‑based AI systems for live sports scenarios, ensuring resilient performance during peak traffic, responsible model behaviour in real time, and an optimal balance between cost, latency, and production‑scale performance. What you'll bring • Proven extensive lead‑level engineering experience delivering sports insights or sports data–driven ML systems, with clear ownership of technical direction, mentoring, and delivery. • Deep understanding of sports data, including hands‑on experience working with event data, tracking data, or other high‑volume sports datasets, and converting these into actionable analytical or predictive insights. • Working knowledge of modern ML techniques, including Generative AI, and how emergent models can extract insights from multi‑modal sports data (e.g., numerical, spatial, video, or metadata). • Advanced Python expertise with strong hands‑on use of ML/DL frameworks (e.g., PyTorch, TensorFlow), including taking models from experimentation into production model serving. • End‑to‑end MLOps experience, including CI/CD for ML, experiment tracking, model registries, drift detection, automated retraining, and infrastructure‑as‑code practices. • Proven technical leadership experience including mentoring and guiding Senior and Mid-Level Data Scientists both in their day to day work and career development. Experience of working in a fast-changing environment is vital demonstrating adaptability and ability to support the team through times of uncertainty, pivoting as necessary. • Experience designing scalable, low‑latency architectures, including real‑time or near‑real‑time data processing (e.g., streaming systems) suitable for live or rapidly evolving sports use cases. • Strong communication skills with the ability to inspire, guide, and clearly articulate complex strategies to executives, cross-functional teams, and stakeholders.