AI Analytics Lead

We are currently seeking a high calibre professional to join our team as a AI Analytics Lead.

In this role you will: 

1. Understanding AI adoption and user behaviour

• Develop a data-backed view of how users (internal and external) adopt and engage with AI-enabled tools and experiences
• Define behavioural frameworks, including segmentation, usage patterns, and progression from basic to advanced usage
• Translate product and experience goals into measurable success metrics, diagnostic metrics, and behavioural indicators
• Move beyond activity-based reporting to understand how AI is shaping workflows, decision-making, and outcomes

2. AI-native experience and journey analytics

• Develop and implement approaches to understand how users move through digital and AI-powered journeys
• Analyse behavioural signals such as task completion, workflow efficiency, repeat engagement, and interaction patterns
• Explore and validate approaches to measure time saved, output quality, and user value
• Combine quantitative and qualitative insight to identify friction points and opportunities to improve experience

3. Enterprise AI tooling and cross-platform analytics

• Build a consolidated view of adoption, usage, engagement, and value across enterprise AI tools
• Identify cross-tool behavioural patterns and opportunities to improve adoption and experience
• Work across product, data, and analytics teams to align metrics, definitions, and measurement approaches
• Bring together fragmented data and insights to create a consistent, enterprise-wide understanding of AI usage and value

4. Data pipelines, dashboards, and analytics infrastructure

• Design and build dashboards, data models, and analytical outputs that provide actionable insight into user behaviour
• Partner with data engineering and analytics teams to shape data pipelines, data models, and data availability
• Develop reusable analytics assets, including metric definitions, reporting templates, and scalable data structures
• Apply advanced analytical techniques (e.g. segmentation, clustering, experimentation) where relevant to deepen insight

5. Product, design, and stakeholder partnership

• Collaborate with product, design, engineering, and data teams to embed measurement into AI-enabled experiences
• Translate insights into clear recommendations that inform product design, prioritisation, and adoption strategies
• Act as a central partner across distributed teams, helping establish consistent approaches to measuring AI success
• Contribute to building a more data-driven, evidence-based approach to both general digital and AI experiences across HSBC

To be successful you will need: 

• Experience and Knowledge:

• Strong experience in product analytics, data science, or advanced analytics within digital or AI-enabled products
• Direct experience working with AI or AI-enabled experiences is required, including understanding how AI shapes user behaviour, workflows, and outcomes
• Experience working across multiple products or platforms, bringing together fragmented data into a coherent, cross-cutting view

• Technical Understanding:

• Strong understanding of data pipelines, data models, and analytics architecture, and how data is captured, structured, and made available
• Hands-on experience with SQL, data analysis, and dashboarding tools (e.g. Power BI, Amazon Quicksight, Looker, etc); experience with Python or similar is a plus
• Experience building scalable analytics assets, including dashboards, metric definitions, and reporting frameworks

• Strategic Thinking & Business Acumen:

• Strong product mindset, with the ability to connect data insights to product, experience, and business outcomes
• Ability to operate in ambiguity and define structure where measurement approaches are still evolving
• Experience influencing decision-making through data-driven insights in a product or platform context

• Governance & Risk Awareness:

• Ability to operate as a hands-on individual contributor while leading through influence across product, data, and engineering teams
• Experience working across distributed data and analytics teams, aligning metrics, definitions, and measurement approaches
• Strong awareness of data governance, privacy, and responsible AI considerations

• Soft Skills:

• Ability to operate as a hands-on individual contributor while leading through influence across product, data, and engineering teams
• Strong communication skills, with the ability to translate complex data into clear, actionable insights for senior stakeholders
• Highly collaborative, with experience working across distributed teams and aligning metrics, definitions, and approaches