Скачать или смотреть Product Manager Interview - The "Zero-Return" Amazon Case study using AI

[The Hook: Start with the pain point]
Amazon has a 30% return problem in apparel and home decor. That’s not just a customer service issue—it’s a multi-billion dollar "Reverse Logistics" tax that burns fuel, capital, and carbon.
[The Context: What this video is]
In this 2026 simulation, we tackle a Senior Product Leader (L7/L8) Interview at Amazon. We move past basic "features" and dive into Systemic Innovation. You’ll see how a top-tier candidate uses GenAI, Neural Radiance Fields (NeRFs), and Amazon’s 25-year data moat to build a "Zero-Return Supply Chain."
[What You’ll Learn - The L7 Rubric]
This isn't just a mock interview; it's a masterclass in the Amazon Leadership Principles (LPs):
✅ Customer Obsession: Moving from "guessing a size" to "predictive fit."
✅ Invent and Simplify: Using "Identity-Linked Sizing" to remove friction.
✅ Frugality: How to scale high-cost GenAI without breaking the GPU budget.
✅ Think Big: Connecting the mobile app UI to the physical warehouse routing.
[Timestamps - The Navigation]
0:00 – The $10 Billion Reverse Logistics Problem
0:50 – The "Prophetic Fulfillment" Strategic Framework
1:30 – Identity-Linked Sizing: Turning Data into a Moat
2:45 – Predictive Inventory Shielding: Connecting AI to the Last Mile
3:50 – Contextual Ambient Rendering (GenAI for Home Decor)
4:45 – Completing the Amazon Flywheel in 2026
[The Engagement Trigger]
👇 The Big Question: Would you feel comfortable with Amazon using your 10-year purchase history to "autocorrect" the size of the clothes you buy? Is it "Customer Obsessed" or "Too Personal"? Let’s debate in the comments!
[Related Resources]
If you’re prepping for a MAANG interview in 2026, remember: They don't hire you for what you can build, they hire you for the trade-offs you can navigate.
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