Retail Merchandising Growth Lead

Job description

Role Title: AI Replenishment Intelligence Lead Location: Delhi, India | On-site | Full-time Mission Brief: Reinvent How Lenskart Never Runs Out — or Runs Over This is not a replenishment role. This is a product transformation mandate . Lenskart operates one of India's largest and fastest-scaling retail networks — thousands of stores, millions of SKUs, and a supply chain that must move at the speed of fashion and the precision of science. Today, our replenishment function is being fundamentally reimagined: from reactive and rule-based to predictive, intelligent, and productized as a scalable AI platform . You will be the architect of that shift. As the AI Replenishment Intelligence Lead , you will own the end-to-end product vision, design, and deployment of Lenskart's AI-driven replenishment platform — a system that ensures the right product reaches the right store at the right moment, every time. You will operate at the intersection of machine intelligence, product management, and retail operations , converting data into decision systems that directly drive availability, profitability, and customer delight across India and global markets. If you've been waiting for a role where AI is not a feature but the core product , this is it. 🔑 Core Mandate 1. AI Platform Development — Build the Brain Own the product vision, roadmap, and lifecycle of Lenskart's replenishment intelligence platform. Define and evolve the product architecture for real-time inventory visibility, ML-driven demand forecasting, dynamic safety stock models, and system-generated replenishment decisions that eliminate manual intervention. Translate complex retail and supply chain problems into clear product requirements, user stories, and technical specifications for Data Science and Engineering teams. Act as the product owner for all AI/ML capabilities within replenishment, ensuring models are production-ready, scalable, and continuously improving. Drive success through clearly defined product metrics such as forecast accuracy (MAPE/WAPE), fill rates, inventory turns, and system adoption — and own these as core product KPIs. 2. Intelligent Assortment & Inventory Optimization — Drive the Business Build and scale AI-powered product features that solve high-impact commercial problems: assortment optimization (store-wise product mix), demand sensing across fashion cycles, and automated markdown and liquidation intelligence. Design systems that dynamically connect inventory decisions with financial and customer outcomes , influencing working capital efficiency, sell-through, and availability. Transform Open-to-Buy into a real-time, system-led product capability , where buying signals are continuously optimized based on live demand, inventory health, and business goals — moving from static planning to always-on decisioning systems . 3. Cross-Functional Leadership — Drive Adoption Drive product adoption and behavioral change across Merchandising, Supply Chain, Finance, and Retail Operations. Ensure the platform is not just built, but deeply embedded into daily decision-making. Act as the voice of the user , continuously refining the product based on stakeholder feedback, usability insights, and operational realities. Build, mentor, and elevate a team of planners and analysts to operate as product users and contributors , fostering a culture of experimentation, data fluency, and trust in AI-led systems. The Essentials Experience: 5–8 years in product management, inventory planning, supply chain product roles, or merchandise planning , with demonstrable experience building or owning AI/ML-driven products or decision systems — not just using them. Technical Depth: Hands-on familiarity with demand forecasting methodologies (time-series models, statistical and machine learning approaches), replenishment algorithm design, and the ability to translate these into scalable product features and system requirements . Education: Degree in AI/ML, Data Science, Operations Research, Engineering, or a highly quantitative field. Top-tier MBA a strong plus. Domain Knowledge: Strong understanding of retail operations, SKU-level planning, supply chain dynamics, and how fashion cycles complicate inventory logic — with the ability to translate these into product constructs and decision frameworks . The Mindset AI-Native Thinking: You don’t add AI to products — you build products around AI capabilities . You understand model strengths and limitations and design systems accordingly. Builder’s Instinct: You treat replenishment as a living product — iterating rapidly, measuring impact, and continuously improving. Analytical Rigor at Speed: You move from ambiguity to clarity fast — translating complex signals into scalable product decisions and features . Transformational Leadership: You drive alignment, build trust in AI systems, and lead product adoption across diverse stakeholders , turning skepticism into advocacy. Equal Opportunity Statement At Lenskart, we are committed to building a diverse, inclusive, and equitable workplace. We welcome applicants from all backgrounds, experiences, and identities — because the best intelligence, human or artificial, comes from many perspectives