Principal Technical Product Manager, Prime Video Commerce Personalization and Member Experience

Hundreds of millions of customers open Prime Video every week. They trust us to help them find something great to watch. Increasingly they expect us to help them discover title offers channel and bundle subscriptions that match their interests.

Today we break that trust in small but compounding ways. A customer declines an offer and sees it again minutes later. A new subscriber gets upsells before finding a show they love. A loyal viewer showing signs of leaving receives a generic message that ignores everything they have ever watched.

We are hiring a Principal PMT to own Commerce Personalization for Prime Video. You will build the intelligence that makes every commerce interaction feel like it comes from a service that actually knows you.

This is a build-and-scale role. You will define what right offer right moment right customer means at Prime Video scale. You will ship systems that remember what customers have told us respect their journey stage and earn the right to make a recommendation. You will prove through measurable customer outcomes that respecting customer drives better business results than pressuring them.

Key job responsibilities
Own the customer problem end-to-end. Define the vision strategy and roadmap for Commerce Personalization across all Prime Video surfaces homepage detail pages checkout notifications and partner storefronts.

Eliminate the contradictions customers experience today. Build coordinated intelligence so a declined offer stays declined at-risk customers get content before commerce and new subscribers arent treated like conversion targets. Define how we measure success and how it impacts our long-term success.

Crack problems others havent drive adoption at scale enabling Partner content discovery for the growing number of channels across 30 countries. Personalized offer timing that converts because its welcome

Ship fast prove impact continuously. Start with the most painful customer contradiction and fix it. Every phase delivers measurable improvement reduced repetition better timing higher engagement among at-risk segments. Build conviction through results.

Work backward from customers to technology. Partner with Applied Science and ML Engineering on reinforcement learning multi-objective optimization and unified customer representations always in service of a specific customer outcome.

Drive organizational alignment. Get multiple teams to optimize shared customer outcomes over local metrics. Win this argument with evidence not authority.

About the team
PV Commerce Personalization & Member Experience sits at the intersection of Prime Videos content experience and its commerce engine. We own the models systems and product experiences that determine which customers see which offers when and how. Our production models including offer propensity churn prediction subscription ranking and cross-offer optimization serve decisions across every Prime Video commerce surface. We are evolving from siloed model optimization to a unified system that coordinates across the full customer journey. We are a product development team that partners closely with cross-functional teams across Prime Video to deliver customer and partner value.

- Experience owning/driving roadmap strategy and definition
- Experience in technical product management program management or engineering
- Experience owning feature delivery and tradeoffs of a product
- Experience with end to end product delivery
- Experience leading engineering discussions around technology decisions and strategy related to a product
- Experience working effectively with science data processing and software engineering teams
- Experience in designing experiments and statistical analysis of results
- Experience owning and delivering end-to-end product strategy for ML/AI-powered consumer products.

- Experience driving direction and alignment with cross-functional teams
- Experience with subscription or commerce business models and LTV-based decision making.
- Familiarity with reinforcement learning multi-armed bandits or multi-objective optimization applied to product personalization.
- Track record of shipping products that changed customer behavior at scale (100M users).
- Experience with experimentation design causal inference and long-term holdout measurement.

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