Data Products & Insights Lead
Who we are
About this role
What you’ll do
- Own the Data Products & Insights strategy, roadmap and prioritised backlog, aligning investment and delivery with Product, Commercial, Operations and customer outcomes.
- Lead the discovery and qualification of use cases, clearly defining users, decisions, value hypotheses, success measures and minimum viable products before delivery begins.
- Own the adoption and full lifecycle of data products, management information, insight services and AI/ML capabilities, including production support and retirement.
- Lead a multi-disciplinary team spanning Data Engineering, Data Science and ML/AI Engineering, maintaining clear responsibilities and effective collaboration.
- Ensure product engineering uses approved platform services, contracts and architecture.
- Ensure Data Science and AI solutions are valid, governed, measurable and ready for reliable production operation.
- Establish measurable customer, commercial and operational value from prioritised data, insight and AI initiatives.
- Lead people, suppliers and cross-functional dependencies, resolving competing priorities and supporting predictable delivery.
- Challenge and redirect demand where there is insufficient evidence, unclear ownership or no sustainable product lifecycle.
- Forecast and control assigned platform supplier capacity and contribute to investment decisions.
- Improve time to value, accuracy, freshness and product lifecycle health while reducing duplicate, manual and low-value reporting.
- Support AI-assisted engineering practices that improve delivery speed and quality while maintaining human accountability, security, privacy, traceability and production discipline.
Who you are
- Significant leadership experience across data products, analytics, Data Engineering, Data Science or AI.
- Demonstrable ownership of product roadmaps, prioritisation, adoption and outcomes beyond project completion.
- Experience leading multi-disciplinary technical specialists and delivery partners.
- A strong understanding of data products, management information, experimentation, model governance and production operations.
- Experience establishing clear responsibilities across Data Engineering, Data Science and ML/AI Engineering disciplines.
- Strong stakeholder and demand-management capabilities, including working effectively with Product, Commercial, Operations and senior leadership.
- Working knowledge of Google Cloud Platform or an equivalent cloud environment.
- Knowledge of data contracts, privacy, security, data sovereignty and responsible AI practices.
- Sound product and commercial judgement when navigating priorities within complex organisations.
- The ability to assess proposed use cases based on ownership, evidence, measurable value and sustainable lifecycle management.
- Experience within telecommunications, digital platforms or customer data would be advantageous.
Not a perfect fit?
What's in it for you
- Shape the strategy and roadmap for a portfolio spanning data products, analytics, Data Science and AI.
- Lead experienced technical specialists across multiple data and AI disciplines.
- Influence how data and AI investment is prioritised against measurable customer, commercial and operational outcomes.
- Work at the intersection of Product, Commercial, Operations and Digital Engineering.
- Help establish scalable, governed and production-ready uses of data and AI across complex digital and technology environments.
- Influence investment decisions and the effective allocation of supplier and platform capacity.
- Improve the adoption and lifecycle management of data products while reducing duplicate, manual and low-value reporting.
What skills you will learn
- Data product portfolio leadership, including roadmap prioritisation, adoption and lifecycle management.
- AI and ML product governance, from use-case qualification through to measurable production performance.
- Multi-disciplinary technical leadership across Data Engineering, Data Science and ML/AI Engineering.
- Cloud data platform knowledge, including the effective use of approved platform services, architecture and data contracts.
- Responsible AI and data governance, including privacy, security, sovereignty and traceability considerations.
- Value-based demand management, using defined users, decisions, hypotheses and success measures to qualify investment.
- Senior stakeholder leadership across Product, Commercial, Operations and technology functions.
- AI-assisted engineering practices focused on delivery quality, accountability and production discipline.
VOIS Equal Opportunity Employer Commitment
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