Data Products & Stewardship Manager, Corporate, SEAA

Chanel; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Descripción de la vacante

Chanel Singapore seeks a Data Products & Stewardship Manager to partner with Corporate Functions and data delivery teams. You will translate business challenges into analytics, data science and AI use cases, and oversee data product lifecycles from ideation to delivery.

You will drive data stewardship, establish clear data definitions, governance, and trusted data sources, while coordinating across data engineering, science and AI specialists to deliver timely, actionable insights.

Formación

• Experience implementing data governance and stewardship across multiple business domains.
• Proven ability to translate business problems into analytics/use cases and data products.
• Strong collaboration with data platforms, engineering, data science and business stakeholders.

Responsabilidades

• Define and manage the lifecycle of corporate data products, dashboards and datasets.
• Lead BI development and coordinate delivery across data engineering, data science and AI teams.
• Establish data definitions, standards and a single source of truth.
• Drive data quality and governance adoption across business teams.

Conocimientos

Data governance

Data quality management

Data analytics

AI/ML concepts

Stakeholder management

Product backlog

BI dashboards

Descripción del empleo

Why This Mission:

The Data Products & Stewardship Manager partners with Corporate Function leaders and data delivery teams, working closely with Strategy & Insight, Finance, HR, Marketing/ PR and Supply Chain to proactively identify and solve business problems through analytics, data science and AI. The role translates business challenges into high-value use cases that accelerate intelligence, activation and value creation in support of SEAA's ambition. By combining internal data with external intelligence and applying developing technical expertise across analytics, data science and AI, the role builds a more comprehensive understanding of business opportunities and guides the development of relevant and scalable solutions. The role also drives data stewardship across Corporate Functions, helping to establish clear data definitions, consistent business logic and trusted sources of data while supporting the effective resolution of data quality issues. It provides coordination and expertise while ensuring that ownership and accountability for data accuracy remain with the business domains and Corporate Functions that produce the data.

Impact You Can Create In The Role:

Data Stewardship and Governance Facilitation: Partner with Corporate Functions and relevant business domains to identify Critical Data Elements, establish appropriate definitions and standards, and resolve related data quality issues. Drive the adoption of data stewardship practices across business teams by embedding clear frameworks, tools and ways of working into day-to-day operations, ensuring governance is applied consistently without creating unnecessary complexity.

Data Quality Management: Partner with business stakeholders to define appropriate Data Quality Rules and reporting for Critical Data Elements. Identify, record and analyse data quality issues and their root causes, partnering with accountable business teams to elevate and drive remediation where required.

Data Product Lifecycle Management for Corporate Functions: Partner with Corporate Function leaders to identify and frame high-value business problems that can be addressed through analytics, data science or AI, supporting growth, improving decision-making and accelerating activation towards SEAA's ambition. Translate business problems into well-defined analytics, data science and AI use cases, specifying the expected business outcome, target users, data requirements, analytical approach and measures of success. Oversee the lifecycle of Corporate Function / Cross-divisional data products, including dashboards, reports, datasets, advanced analytics and AI-enabled products, from ideation and experimentation through delivery, adoption and continuous improvement. Ensure that solutions provide relevant, timely and actionable insights for business decision-makers. Own and actively manage the product backlog, determining which data products and use cases should be prioritised, phased or declined based on business value, strategic alignment, feasibility, data readiness and resource capacity. Partner with Data Platform & Engineering and specialist teams, which own detailed technical design and implementation. Lead the BI development team and coordinate delivery across data engineering, data science and AI specialist teams, ensuring timely and high-quality delivery of data solutions. Ensure business requirements are clear, granular and ready for engineering and analytics teams to deliver with minimal rework. Drive adoption of data products, strategic KPIs and self-service analytics capabilities by guiding business users and promoting consistent use of a single source of truth.

Applied Analytics, Data Science & AI: Assess the suitability and feasibility of different analytical approaches, working with technical specialists to determine when descriptive analytics, predictive modelling, optimisation or AI-enabled solutions are appropriate. Support the design, experimentation and validation of data science and AI use cases, constructively challenging proposals to ensure that business value, user needs, data readiness, responsible AI requirements and implementation feasibility are addressed. Identify and integrate relevant internal data and external intelligence sources, including databases, market research and industry insights, to strengthen business analysis, opportunity identification and decision-making.

Your Success Measures

• Value Creation through Advanced Analytics, Data Science and AI: Analytics, data science and AI solutions address clearly defined business problems and demonstrate measurable improvement in decision quality, operational effectiveness, client experience or business performance.
• Data Quality & Fit-for-Purpose: Corporate data assets are accurate, complete, consistent and fit for their intended use, with material data quality issues escalated and resolved in a timely manner.
• Adoption & Usage: Increased usage of dashboards, datasets and data products by target bu