Data & AI Business Enablement Lead

Richemont, one of the world leaders in the luxury sector, has various Houses specializing in jewelry, watches and high-end accessories. Each Maison proudly embodies a tradition of style, quality and craftsmanship and Richemont strives to preserve the heritage and identity specific to each of them. At the same time, we are committed to innovating and designing new products in line with the values ​​of our Houses, through a process of permanent creativity.

MAIN PURPOSE

This position engages with Maisons, functions, key users and user communities in Japan to understand business challenges and user needs related to data, analytics and AI, and translate them into clear requirements and executable initiatives.

For assigned projects and services, the role manages the end-to-end journey from demand and requirements through implementation, business validation, release, adoption, sustained use and value realization, connecting business users with technical teams, global and regional teams, and external partners.

The role also holds Level 2 business and user-facing accountability for assigned data, analytics and AI services, aiming not merely for go-live but for solutions to be used sustainably in daily work and deliver the intended business outcomes.

KEY RESPONSIBILITIES

1. Business Demand and User Needs Management

Maintain ongoing dialogue with Maisons, functions, key users and user communities to understand business challenges, pain points, user needs, key performance indicators and opportunities to apply data, analytics and AI.

Structure business needs into clear business requirements, data and analytics requirements, AI use cases or service requirements.

Structure business value, urgency, user impact, readiness and dependencies, and provide the information required for initiative prioritization.

Clarify the business problem to be solved and the expected outcome without presupposing a specific technology, and evaluate solution options with technical teams.

Clarify Japan user needs, gaps and adoption requirements for data, analytics and AI capabilities provided by global and regional teams.

2. Project Implementation and Business Readiness

Manage assigned data, analytics and AI projects or workstreams from requirements definition through implementation, business validation, release and operational adoption.

Coordinate delivery with the Data & AI Platform Lead, other technical teams, global and regional teams, and external partners to ensure business requirements are appropriately reflected in the solution.

Plan and coordinate user acceptance testing, business validation, data and report validation, migration, release, cutover and business readiness.

Continuously manage scope, schedule, issues, dependencies, user readiness and delivery risk, escalating matters beyond the delegated scope appropriately.

Confirm that business owners, key users, support routes, documentation, training and operational handover are clear when introducing new or changed services.

3. Adoption, Enablement and Value Realization

Drive adoption of new and changed data, analytics and AI solutions through training, onboarding, communications, user guides and key-user enablement.

Treat go-live as a milestone rather than the completion criterion, and continuously monitor usage, adoption, user feedback, user self-sufficiency and expected business outcomes.

Enable key users and user communities to use data, analytics and AI effectively in daily work.

Structure usage data, user feedback, business feedback and service issues, and feed them into improvement backlogs and subsequent enhancements.

Increase user data and AI literacy and self-sufficiency, turning one-time project delivery into sustained use.

4. Level 2 Business Service Ownership and Continuous Improvement

Manage Level 2 user-facing issue resolution, service quality, escalation and operational acceptance for assigned data, analytics and AI services.

Structure reporting issues, data and business definition issues, user issues, adoption issues and business-process issues, coordinate the required stakeholders, and drive resolution.

When a technical root cause or platform action is required, work with the Data & AI Platform Lead and relevant technical teams while maintaining coordination and follow-through with users and the business.

Analyse incidents, recurring issues, user feedback, usage and service data, and convert insights into improvements in reporting reliability, usability, documentation, support processes, training and adoption.

Maintain service coordination for Japan users for assigned services even when execution is performed by global or regional teams or external service partners.

5. Stakeholder Coordination and Operational Management

Manage communication and coordination among business users, key users, technical teams, global and regional teams, and external partners for assigned projects, services and workstreams.

Ensure execution follows agreed governance, security and compliance standards, delivery processes and escalation paths.

Make workload, priorities, issues, dependencies, service status and improvement actions visible within the assigned scope and report them regularly.

Share insights from business users with the Data & AI Platform Lead and relevant teams to improve platforms, data quality, reusable solutions and support models.

Structure the required information and escalate priority trade-offs, major delivery risks, resource constraints or strategic decisions beyond the delegated scope to senior decision-makers.

COMPETENCIES AND EXPERIENCES

・Professional experience in business requirements, implementation, user support or adoption within data, analytics, business intelligence, AI or a related digital service.

・Ability to structure business-user challenges and ambiguous needs into clear requirements, use cases and executable work items.

・Experience with requirements, testing, release, migration, adoption and ongoing operations for data, analytics or digital projects and services.

・Experience coordinating business users, technical teams, global and regional teams, and external partners to drive issues through to resolution.

・Practical experience in user acceptance testing, business validation, training, onboarding, or change and adoption activities.

・Ability to structure data and reporting issues from data, business-process and technology perspectives and resolve them in collaboration with technical stakeholders.

・Ability to identify recurring issues and improvement opportunities from incidents, user feedback, usage data and service data, and convert them into continuous improvement.

・Ability to lead stakeholder management, issue management, planning, documentation and reporting proactively and in a structured manner.

Ability to continuously learn about data, analytics and AI technologies and business applications and explain them clearly to users.

・Business-level English and native-level Japanese.

・Bachelor’s degree or equivalent professional experience.

・Experience with Power BI, Looker, Tableau, SAP Business Intelligence or equivalent business intelligence and analytics platforms.

・Foundational knowledge of cloud data platforms, data models, data pipelines and modern analytics architecture, plus SQL or equivalent data-analysis skills.

・Practical experience in data governance, KPI and business definitions, data quality or data ownership.

・Experience in business validation, migration or user adoption when moving from legacy reporting and analytics environments to modern analytics or cloud data platforms.

・Experience participating in generative AI, AI agent or enterprise AI initiatives, or practical knowledge of and interest in applying AI to business problems.

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