Retail Merchandising Growth Lead

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 aproduct 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 topredictive, intelligent, and productized as a scalable AI platform.

You will be the architect of that shift.

As theAI Replenishment Intelligence Lead, you willown 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 ofmachine 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 whereAI is not a feature but the core product, this is it.

🔑Core Mandate

1. AI Platform Development — Build the Brain

Own theproduct vision, roadmap, and lifecycle of Lenskart's replenishment intelligence platform. Define and evolve theproduct 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 intoclear product requirements, user stories, and technical specifications for Data Science and Engineering teams. Act as theproduct owner for all AI/ML capabilities within replenishment, ensuring models are production-ready, scalable, and continuously improving.

Drive success throughclearly 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 scaleAI-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 connectinventory decisions with financial and customer outcomes, influencing working capital efficiency, sell-through, and availability.

Transform Open-to-Buy into areal-time, system-led product capability, where buying signals are continuously optimized based on live demand, inventory health, and business goals — moving from static planning toalways-on decisioning systems.

3. Cross-Functional Leadership — Drive Adoption

Driveproduct 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 thevoice 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 tooperate as product users and contributors, fostering a culture of experimentation, data fluency, and trust in AI-led systems.

The Essentials

Experience: 5–8 years inproduct management, inventory planning, supply chain product roles, or merchandise planning, with demonstrable experiencebuilding 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 intoscalable 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 intoproduct constructs and decision frameworks.

The Mindset

AI-Native Thinking: You don’t add AI to products — youbuild products around AI capabilities. You understand model strengths and limitations and design systems accordingly.

Builder’s Instinct: You treat replenishment as aliving product — iterating rapidly, measuring impact, and continuously improving.

Analytical Rigor at Speed: You move from ambiguity to clarity fast — translating complex signals intoscalable product decisions and features.

Transformational Leadership: You drive alignment, build trust in AI systems, andlead 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