Are you seeking an environment where you can drive innovation? Do you want to apply machine learning techniques and advanced statistical modeling to solve real world problems in one of the world’s largest Supply Chain Management systems? Do you want to play a crucial role in the future of Amazon's Consumer business?
The Inbound Capacity team is part of Amazon’s Supply Chain Optimization Technology/Inbound Systems organization, and is responsible for optimizing the usage of storage and inbound capacity of Amazon’s fulfillment network to maximize the long term free cash flow for the company. We generate capacity control signals for Amazon’s automated supply chain system to ensure that their decisions always stay within the available capacity for each marketplace as well as the fulfillment centers supporting it. We achieve this objective using advanced science technologies in simulation, machine learning and optimization.

As an Applied Scientist in the team, you will operate at the apex of multiple advanced Amazon systems, getting global visibility of how Amazon functions and serves our customers. Your curious mind will enable the creation of products that drive ever-greater automation, scalability and optimization of every aspect of capacity management at Amazon, removing cost and delivering speed of execution to thrill our customers. The impact of your work will be global, material and remarkable. We will develop new statistical and machine learning techniques in prediction, clustering, anomaly detection, learning based decision making, etc. Detailed responsibilities include but not limited to:

Build ML models to predict production systems’ behaviors.
Collaborate with our software team to create scalable production implementations for large-scale data pipeline.
Develop an understanding of key business metrics/KPIs and providing clear, compelling analysis that shapes the direction of our business.
Presenting research results to our internal research community.
You will research and implement novel machine learning and statistical approaches. Your contributions will be seen and recognized broadly within the Amazon Retail organization, contributing to the Amazon research corpus and patent portfolio.