Responsibilities
* Lead the technical strategy and delivery of production machine‑learning systems that transform raw sports data and live video into real‑time insights and personalised experiences for millions of fans.
* Shape roadmap, architecture, and platform evolution for critical ML domains such as live sports insights, real‑time ranking, computer‑vision for multi‑angle video, and streaming inference.
* Mentor engineers and data scientists, raising the bar across teams.
* Design, develop, and operationalise AI solutions using computer vision, machine learning, generative AI, and data science to enable automated sports metadata generation and key‑event detection in live content streams.
* Generate actionable insights for player performance, contextual statistics, and injury risk while embedding responsible and ethical AI principles throughout the design‑to‑deployment pipeline.
* Integrate model‑driven insights into personalisation engines, tailoring recommendations based on teams, players, match context and other signals, ensuring transparency, fairness and appropriate data use.
* Define experimental designs, lead A/B testing, develop and maintain metrics and dashboards, and establish robust MLOps practices.
* 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, balancing cost, latency and production scale.
* Hybrid working: 2 days a week onsite at the Osterley office.
Qualifications
* Proven extensive lead‑level engineering experience delivering data‑driven ML systems, with clear ownership of technical direction, mentoring and delivery.
* Working knowledge of modern ML techniques, including generative AI, and how emergent models can extract insights from multimodal sports data (numerical, spatial, video, metadata).
* Advanced Python expertise with hands‑on use of ML/DL frameworks such as PyTorch and TensorFlow, including taking models from experimentation to 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 guiding senior and mid‑level data scientists in day‑to‑day work and career development.
* Experience working in a fast‑changing environment, demonstrating adaptability and the ability to support the team through uncertainty.
* Nice to have: understanding of sports data, hands‑on experience with event data, tracking data or other high‑volume sports datasets, and converting them into actionable analytical or predictive insights.
* Passion for sport and desire to push the sports experience to the next level is a real bonus.
EEO Statement
We are a Disability Confident Employer and welcome and encourage applications from all candidates. We will ensure a fair and consistent experience for all, and make reasonable adjustments to support you where appropriate.
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