Head of Data Science

📋 Description

• Prioritise which opportunities have the biggest impact.. Build a value-based use-case backlog
• Personally deliver the first system end to end: the analysis and back-test, the features, the
• Operate what you deploy. Own monitoring, retraining, drift response, incident handling and rollback
• Work across both traditional ML and agentic systems, and be honest about which a problem actually
• Build and lead the team — hire senior data scientists embedded in value areas and machine learning
• Establish the production stack alongside Data Platform and Engineering: reproducible training data

🎯 Requirements

• Experienced leading data science or ML teams that own systems in production, with deployments that
• Hands-on now, not formerly. Your first quarters are spent writing SQL and Python, engineering
• Experienced across both traditional ML and agentic systems, and clear about how they differ in
• Practised at running live systems rather than just launching them — monitoring, retraining
• Pragmatic about method: happy to argue for rules or heuristics where they capture most of the
• Comfortable with an undefined starting point. There's a long list of candidate use cases and a

🎁 Benefits

• Remote work options (digital-first company)
• Unlimited holidays
• 4 months paid family leave regardless of gender
• Annual learning fund and personal development opportunities
• Exposure to new challenges and internal/external training
• Inclusive and collaborative culture