I build Fast Supply Chains — literally.
Senior Product & Program Leader with 8+ years at Amazon across India and the UAE, driving end-to-end ownership of high-impact e-commerce and quick commerce platforms. Currently leading Products, CX and Special Initiatives for Same-Day Quick Commerce across India & UAE, with full lifecycle accountability from strategy and roadmap to launch and scale.
Deep expertise in 0-to-1 development across the entire e-commerce value chain — Selection, Availability, Inventory Placement, Forecasting, Network Design, Space Optimization, and Last-Mile Delivery.
Measurable outcomes delivered across supply chain, product, and operations
Across product strategy, supply chain, and data domains
10+ years across product, program, and operations
Vibin is a true people's manager who really knows how to get the best out of his team members. He demonstrates a high level of ownership, frugality and Bias for actions while delivering results for our customers. He also motivates and encourages his team members and his stakeholders in building a team that is more customer centric. So for all these qualities which he possesses makes him more eligible to be Senior Program Manager at Amazon.
Vibin is an excellent manager. I had a pleasure working for close to two years with him, and he truly leads by example. He always kept the team morale up, and I believe he is the best manager I ever had. His ability to work through the crisis and develop new ways to achieve the targets were always inspiring. He is one of the managers who would sit and make you learn new things and work on your professional skills.
Looking for senior product leadership roles in e-commerce, logistics, or supply chain platforms.
End-to-end ownership from strategy and roadmap to launch and scale. Tap any card for the full case.
Designed and scaled the same-day quick commerce supply chain platform — from topology and warehouse placement to pick-by-route, multi-modal dispatch, and continuous routing.
Launched scheduled delivery capability in India enabling customers to choose desired delivery slots — transforming default delivery into a flexible, customer-first model while driving significant capacity gains.
Transitioned grocery from a siloed app-in-app model to fully integrated in-app same-day delivery with single-order, single-shipment fulfilment.
AI-powered routing engine that dynamically matches orders with the optimal logistics channel (bike, van, truck) based on geocode, product attributes, and real-time utilisation.
Proprietary ML model generating 4-week, hourly-granularity demand predictions with weather-based forecasting, integrated with real-time capacity alerts and cross-utilisation engine.
A swipe-native mobile app that solves streaming's discovery problem through a custom behavioral AI recommendation engine. Built 0-to-1 — React Native, Supabase, real-time preference learning, cold start handling, and an anti-filter-bubble exploration system. Full product ownership across strategy, UX, AI design, and technical execution.
The quick commerce market requires a spectrum of delivery speeds. The fundamental challenge: the closer your fulfilment infrastructure is to the customer, the higher your operational investment — yet the more constrained your product selection becomes.
Built a topology product leveraging demand heatmaps at geocode level to identify optimal warehouse placement. Developed a "pick-by-route" system where associates receive pick lists in reverse bay order aligned with delivery routes. Integrated MMOT across bikes, vans, and trucks into a unified dispatch system. Designed and ran A/B tests and phased rollouts to validate each feature launch.
Platform now fulfils 146M+ orders annually across the top cities in India and the Middle East, with a 57% improvement in customer promise speed. Customer conversion to same day delivery improved by 18%.
The existing same-day delivery model offered only default delivery windows, leaving capacity underutilised in off-peak slots and creating friction for customers who wanted more control over their delivery.
Launched scheduled delivery capability enabling customers to choose their desired delivery slot. Required integration across the ordering system, capacity planning tools, and routing platforms to make slots available dynamically based on real-time capacity.
Improved customer conversion and increased capacity utilisation by 27%, directly reducing cost-to-serve. Customers gained more control and the network achieved better load distribution across delivery windows.
Amazon's grocery offering was a siloed app-in-app model creating a fragmented experience. Separate orders for grocery and non-grocery items required multiple delivery riders for what could have been a single shipment.
Designed an integrated experience incorporating grocery and fresh food into the main app in a single shopping journey. Introduced a "single order, single shipment" model requiring substantial integration across WMS systems, last-mile platforms, and logistics coordination tools.
Enabled 8,000+ new SKUs and drove 18% improvement in conversion. Achieved a double-digit reduction in total customer shipments while maintaining the same order demand.
The logistics network operated exclusively with bike-based delivery agents, limiting access to peripheral zones and preventing efficient handling of large products. Manual routing interventions were neither scalable nor sustainable.
Developed an AI-powered routing system that dynamically matches orders with the most appropriate logistics channel (Bike, Van, Truck). Synthesises geocode locations, product attributes, and real-time resource utilisation metrics to balance efficiency with delivery speed.
Eliminated manual planning overhead entirely. Increased resource utilisation by 12% through data-driven optimisation. Generated millions in annual cost savings while enhancing service quality and operational predictability.
Traditional manual forecasting cannot scale with modern fulfilment operations. Legacy approaches couldn't adapt to changing demand signals or weather events, introducing inefficiencies that directly impacted customer satisfaction.
Developed a proprietary ML forecasting model generating demand predictions for four weeks at hourly granularity. Integrated weather-based forecasting module. Operating on an hourly refresh cycle synthesising inventory availability, fulfilment turnaround times, and multi-channel utilisation patterns.
Improved demand planning accuracy by 17%. Saved 6,000+ manager hours annually. Deployed and scaled across India, GCC, and Japan markets through a unified control tower.