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Key Responsibilities

• Analyze operational, sales, and inventory datasets to surface trends, risks, and business opportunities with clear commercial implications.
• Translate business questions into analytical frameworks and deliverables — dashboards, KPI trackers, exception reports — that non-technical stakeholders can act on.
• Provide data-driven recommendations that directly support business performance, supply chain efficiency, and operational decision-making.
• Build and maintain dashboards across key business KPIs (sales performance, stock efficiency, product availability, service levels, replenishment).
• Develop standardized reporting frameworks for real-time and scheduled business reviews.
• Partner with Data Engineers and Data Scientists to validate data pipelines, ensure data accuracy, and shape the BI semantic layer and data mart design.
• Maintain business metric definitions, data dictionaries, and documentation for analytics datasets.
• Support automation initiatives including exception-based management and reporting automation; help streamline manual reporting processes across business teams.

What We're Looking For

• Bachelor's degree or higher in a quantitative or business-related field — Analytics, Statistics, Mathematics, Supply Chain, Economics, or equivalent. Strong academic performance is a plus.
• 1+ years of experience in data analysis, business analytics, or BI roles, with a track record of working directly with business stakeholders — not just delivering technical outputs.
• Solid SQL skills; proficiency with at least one BI platform (Power BI, Tableau, Looker, or equivalent).
• Demonstrated ability to understand business context: you know what the numbers mean for the P&L, not just what the query returns.
• Experience in industries where operational complexity matters — FMCG, Retail, Supply Chain, F&B, or Consumer Products. Candidates with Masan-adjacent domain exposure are strongly preferred.
• Strong communication skills — able to present data narratives to commercial and operational leaders clearly and confidently.

Nice to Have

• Hands-on experience with Supply Chain analytics: demand forecasting, inventory planning, replenishment, or DC/warehouse-to-store allocation.
• Exposure to Data Science or ML forecasting workflows.
• Experience with Python or R.
• Familiarity with cloud data platforms (Azure, GCP, Snowflake).