AI/ML Data Science Engineer

Location: South East London / Kent (hybrid - 3 days on-site)
Type: Permanent
Salary: Up to £88,000 + bonus
Sector: Global sports & entertainment / live event technology
A world-leading, globally recognised sports & entertainment organisation is hiring an AI/ML / Data Science Engineer to join its Innovation & Technology function. This role will design, build, and deploy intelligent data products and AI/ML solutions that support real-time operations, media/broadcast experiences, and digital fan engagement.

What you'll be doing

AI & Machine Learning

• Build and deploy ML models across:
  • Predictive analytics (performance trends, forecasting)
  • Computer vision (video analysis, media enhancement)
  • NLP (fan insights, content intelligence, knowledge systems)
  • Anomaly detection (telemetry/operations monitoring)
• Build scalable inference pipelines (real-time + batch)
• Implement monitoring, drift detection, and retraining workflows
• Prototype quickly to prove value, then scale into production
• Shape problem definitions with stakeholders in ambiguous environments
• Bring best practices from adjacent industries into a high-performance setting

Data Engineering & Architecture

• Design/maintain pipelines integrating telemetry/timing, video/image, event operations, and digital/commercial engagement data
• Develop APIs/services to expose analytics internally
• Ensure high availability and low latency where required

Cross-functional delivery

• Partner with Broadcast/Media, Operations, Commercial/Marketing, Cybersecurity/IT
• Communicate clearly to technical and exec audiences; become a trusted AI/data partner

Governance, compliance & security

• Responsible AI practices, privacy/governance support, and secure-by-design delivery with cyber teams

What we're looking for

Essentials

• 5-10 years' experience in data science / AI / ML engineering
• Strong Python
• Production ML deployment experience (end-to-end)
• Strong SQL + modern data warehouse experience
• Cloud familiarity + containerisation/orchestration
• Streaming data knowledge
• Solid stats/probability + evaluation + feature engineering
• Strong stakeholder communication + documentation; comfortable in event-driven environments

Desirable

• Real-time data environments; CV/video analytics; edge/low-latency; telemetry/IoT; sensor fusion; cybersecurity exposure