PM-TMgr,KnowledgeProducts

Amazon's Last Mile delivery network moves billions of packages a year. When a delivery station misses its performance target, the question isn't “what” happened- it's “why”, and “what” we should do about it. That's the problem you'll solve. You'll own the knowledge and attribution platform that sits underneath all our AI and analytics products. Think of it as the structured brain that makes our AI specific instead of generic. When our AI assistant tells a station manager exactly why productivity dropped 10% and gives them four prioritized actions to close the gap by tomorrow instead of a vague summary - it's because the knowledge layer you built made that precision possible. When a VP reads an auto-generated business review and every claim traces back to a verified source with zero fabrications, it's because the ontology you govern enforced that consistency. This role is different from most PM-T roles because you won't own a surface - a dashboard, an app, a feature. You'll own the depth. The structured knowledge, causal logic, and self-service platform that an entire portfolio of AI products depends on. Your decisions about how metrics are defined, how causal relationships are modeled, and how teams contribute new knowledge will determine whether our AI products are trusted or ignored. Key job responsibilities - Build and govern the enterprise knowledge system. You'll define the canonical metric definitions, causal relationships, and operational context models that every AI and analytics product consumes. Every Concept will mean the same thing in every dashboard, every business review slide, and every AI response - because you made it so. You'll scale this across operational domains (routing, capacity, labor planning, delivery execution) and resolve the genuinely hard problem of conflicting definitions across organizations. - Own the causal attribution framework. We maintain root-cause attribution systems that decompose metric misses into specific, quantified driver