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  "job_description": "Data Product & ML Management Lead\n\nLondon – Hybrid\n\n£105,000 - £125,000 + bonus\n\nVIQU have partnered with a major UK brand as they expand their data capabilities and invest further in AI and machine learning. They’re hiring for a Data Product Lead to drive the development and delivery of scalable data and ML products that support key business goals.\n\nKey Responsibilities of the Data Product & ML Management Lead:\n• Lead the strategy and delivery of data and machine learning products across the organisation.\n• Own the full product lifecycle, ensuring delivery, governance, and performance tracking.\n• Partner with data science and engineering teams to develop scalable ML products that are production-ready.\n• Collaborate with business stakeholders to prioritise product roadmaps and deliver measurable outcomes.\n• Drive best practices in data product governance, security, and compliance.\n• Champion continuous improvement through user feedback and product performance data.\n\nKey Requirements of the Data Product & ML Management Lead:\n• 5+ years’ experience in data product management, with exposure to AI/ML product delivery.\n• Strong understanding of Azure and Databricks platforms.\n• Strong insurance industry background.\n• Experience deploying machine learning products in a business setting.\n• Track record of setting up governance frameworks for data and ML products.\n• Excellent communication and stakeholder engagement skills.\n• Experience working in Agile or continuous delivery environments.\n• Comfortable working closely with data scientists, engineers, and business leaders.\n\nTo discuss this opportunity in more detail, please APPLY NOW for a no obligation chat with your VIQU Consultant. Alternatively, you can contact Katie Dark via the VIQU website.\n\nFollow VIQU IT Recruitment on LinkedIn and Twitter @VIQU_UK to hear about other opportunities and recruitment news.\n\nData Product & ML Management Lead\n\nLondon – Hybrid\n\n£105,000 - £125,000 + bonus",
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  "job_posted_at_datetime_utc": "2026-03-18T00:00:00.000Z"
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