Customer AI & Analytics Product, Principal (Senior Manager)

At AIA we’ve started an exciting movement to create a healthier, more sustainable future for everyone.

As pioneering innovators for over 100 years, we’re now transforming our organisation to be faster, simpler and more connected. Because we want to be even better equipped to develop digital solutions and experiences that help more people live Healthier, Longer, Better Lives.

To get there, we need people with tech/digital/analytics expertise and passion to help develop positive, sustainable change through digitally enhanced experiences that will impact the lives of millions of people and create a healthier future for everyone.

If you believe in developing a better tomorrow, read on. 

About the Role

Customer Centricity - This role contributes to the delivery of customer outcomes that are reliable, thoughtful, and create meaningful impact for customers and society. The role holder is expected to embed a customer first mindset in all decisions and actions by developing a clear understanding of customer needs, taking end to end ownership to resolve issues, and working collaboratively across teams to continuously enhance the customer journey—both directly and indirectly.

• Define and own the upstream data collection strategy across AIA HK's digital and customer touchpoints — determining what data needs to be captured, where, and in what form — so that the organisation has a clean, complete, and trustworthy foundation for data science and propensity modelling.
• Act as a trusted partner and influencer: build win-win relationships with IT, Data Engineering, Data Science, and other business data owners to align priorities, remove barriers, and co-create outcomes — operating without direct authority over any of these teams.
• Translate business intelligence needs into clear data collection requirements and champion these requirements through IT and engineering teams; hold the business perspective in every data instrumentation decision.
• Ensure all telemetry instrumented across digital assets is coherently designed so that collected data can flow seamlessly into propensity and analytics models — without gaps, duplication, or schema inconsistencies.
• Lead the AI transformation of internal Customer & Marketing processes: champion agentic AI tools (including GitHub Copilot and equivalent) to reduce manual overheads, and work hands-on with business people to build AI skills and capabilities as a force multiplier for the department.
• Ensure Customer Centricity — mandatory: every data collection and partnering decision must be grounded in a measurable improvement to the customer or agent experience.

Roles and Responsibilities:

Data Collection Definition & Requirements

• Define what data needs to be captured across each digital and customer touchpoint (web, mobile, CRM, policy systems, campaign channels) and specify the precise events, attributes, and context required for downstream analytics
• Own the event taxonomy and data collection standards: document business requirements in structured data specifications and hand these to IT and engineering teams for instrumentation
• Review and sign off on IT's implementation of telemetry to ensure data collected matches the agreed specification; identify gaps and raise change requests
• Maintain the data collection registry: a living catalogue of what is being captured, where, and for what analytical purpose
• Ensure data stitching is achievable: specify the identity linkage requirements (anonymous-to-authenticated, cross-device, cross-system) so that IT can implement a unified customer view

 

Cross-Functional Partnering & Influencing

• Build and maintain strong working relationships with IT, Data Engineering, Data Science, Marketing, Distribution, Customer Experience, and other business data owners — operating as a trusted partner rather than a gatekeeper
• Influence without authority: drive data collection priorities onto IT and engineering roadmaps by building a compelling business case, demonstrating downstream value, and creating shared ownership of outcomes
• Facilitate regular alignment forums between business data consumers (Marketing, Analytics) and data producers (IT, engineering): surface conflicts early, broker compromises, and ensure all parties leave with agreed actions
• Identify other business units that hold complementary data assets; negotiate data sharing agreements and access arrangements that create mutual value — no silo thinking
• Build personal credibility with both technical and business stakeholders by demonstrating a thorough understanding of each party's constraints, incentives, and success measures
• Resolve competing priorities diplomatically: when IT capacity is constrained, work collaboratively to sequence data collection requirements in a way that delivers the highest combined business impact

 

Propensity Model Data Enablement

• Partner with Data Science teams as the data subject-matter expert: ensure the upstream data collected is fit-for-purpose for building propensity models (likelihood-to-buy, likelihood-to-lapse, cross-sell, campaign response)
• Define and document the data inputs needed for each propensity use case; translate model feature requirements back into data collection requirements for IT
• Ensure traceability: every propensity data input must be traceable back to a defined collection point and a business requirement
• Monitor data quality at the collection layer — completeness, freshness, and consistency — and escalate remediation collaboratively with IT when data does not meet model-readiness standards

 

AI Transformation & Agentic AI Leadership

• Lead the AI transformation of internal Customer & Marketing department processes: identify manual, repetitive, or low-value workflows and own the design and delivery of agentic AI solutions to replace or augment them
• Champion and demonstrate hands-on use of agentic AI tools including GitHub Copilot and equivalent platforms; act as the department's resident AI capability builder
• Work directly with business stakeholders to co-design AI-assisted workflows; build AI literacy and prompt-engineering skills within the team as a force multiplier
• Define the AI transformation roadmap for the department: prioritise use cases by impact and feasibility; track adoption metrics and efficiency gains

 

Data Governance & Communication

• Establish and maintain data collection governance: ensure all data captured complies with PDPO/GDPR requirements and AIA data privacy policies; obtain business sign-off before any new data collection is instrumented
• Communicate data collection status, gaps, and quality issues to senior stakeholders in plain business language; frame issues as shared problems with proposed solutions rather than IT or business blame
• Advocate for data democratisation: work with Data teams to make collated customer data accessible for analysis by Marketing and Customer teams

Minimum Job Requirements:

Technical Skills:

• Data collection requirements definition and business-to-IT translation — ability to write precise data specifications, event taxonomies, and data contracts
• Customer data concepts: identity resolution, data stitching, customer journey mapping, and telemetry design principles (awareness of tools such as Segment, Amplitude, GA4 or equivalent is beneficial but implementation is owned by IT)
• Understanding of data science and propensity modelling concepts — sufficient to define the upstream data inputs that models need, without building models directly
• Agentic AI tools and LLM-based automation (GitHub Copilot, Azure OpenAI, or equivalent); hands-on experience building or deploying AI-assisted workflows
• Data governance and privacy frameworks (PDPO, GDPR concepts); data quality assessment
• Business analysis and requirements documentation (user stories, data specifications, acceptance criteria)

Behavioural Skills:

• Influencing without authority — proven ability to drive outcomes across IT and business teams where no formal reporting line exists; skilled at building the business case that makes others want to act
• Cross-functional partnership and win-win negotiation — able to identify shared interests between competing stakeholders and broker agreements that advance collective goals
• Business-to-IT bridge builder — translates ambiguous business intelligence needs into precise, implementable data requirements that IT teams can act on confidently
• AI transformation leadership — ability to champion and deliver agentic AI solutions hands-on alongside business teams
• Stakeholder management and executive communication — comfortable presenting to senior business and technology leaders, adapting language for each audience
• Attention to data quality and completeness — a practitioner's instinct for spotting gaps in data coverage before they become modelling problems
• Customer empathy and privacy-first thinking

Experience:

• 5–8 years in customer data, digital analytics, business analysis, or data product roles spanning both business and technology-facing environments; demonstrated track record of influencing IT or engineering teams to deliver data outcomes without direct authority; familiarity with propensity modelling or data science workflows as a data provider (not necessarily as a modeller); experience in financial services or insurance preferred.

Education:

• Bachelor's degree in Business, Marketing, Information Management, Data Analytics, or related field.

Certifications:

• Data governance certifications, CDP awareness (e.g. mParticle, Segment), GitHub Copilot / Azure AI fundamentals, or digital analytics certifications preferred.

Customer Centricity:

• Ensures all data collection and partnering decisions improve measurable customer and agent outcomes, with full privacy and consent compliance.

 

Others:

• You are required to acquire the relevant license(s) if your job involves regulated activities

Build a career with us as we help our customers and the community live Healthier, Longer, Better Lives.

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