Associate - Instock Analyst

Role Overview

The Instock Analyst will be part of the MNow (Dark Store / Quick Commerce) Planning team, responsible for ensuring optimal stock availability across dark stores. This role blends inventory analytics, demand-side judgement, and hyperlocal planning to minimize stockouts and overstocks while supporting adherence to the DS (Data Science) replenishment model.

Key Responsibilities

• Monitor and analyze dark-store inventory health across cities using availability, sell-through, and sales-plan-vs-actual metrics to flag misallocation and stockout/overstock risk.
• Identify inventory redistribution opportunities within and across dark stores/cities to improve fill rate and reduce lost sales.
• Track adherence to the DS (Data Science) replenishment model and work with cross-functional stakeholders (Data Science, Category, Supply Chain) to resolve dependencies affecting model output — including sales plan inputs, store design alignment, FDC replenishment, RGM optimization, dark store RSTN, sourcing, and Mnow replenishment via Vulcan movement.
• Build and maintain local/hyperlocal demand signals to inform stock placement decisions at the store level, accounting for city- and store-specific demand patterns.
• Develop and refine availability planning processes to proactively identify and close instock gaps ahead of demand spikes (events, weekends, seasonal shifts).
• Perform advanced data analysis on inventory, sales, and demand datasets to generate actionable insights and recommendations for planning and category teams.
• Prepare regular reporting and dashboards summarizing instock performance, root causes of gaps, and progress on resolution.
• Present findings and recommendations to senior stakeholders in a clear, solutions-oriented manner.

Qualifications

• Bachelor's degree in Engineering, Statistics, Business Analytics, Supply Chain, or related field.
• Prior experience in inventory planning, retail analytics, or quick-commerce/dark-store operations preferred.
• Strong proficiency in Excel/SQL; familiarity with BI tools (e.g., Tableau, Power BI) a plus.
• Comfort working with large datasets and translating analysis into operational recommendations.
• Ability to collaborate across functions (Data Science, Category, Supply Chain, Store Ops) and communicate clearly with senior stakeholders.