Have you ever ordered a product on Amazon, and when that box-with-the-smile arrives, you wonder how it got to you so fast? Wondered where it came from and how much it would have cost Amazon? If so, Amazon's Technologies (SCOT) team is for you. We build systems to peer into the future and estimate the distribution of tens of millions of products every week to Amazon's warehouses in the most cost-effective way. This team focuses on saving hundreds of millions of dollars using cutting edge science, , and scalable distributed software on the Cloud that automates and optimizes and shipments to customers under the uncertainty of demand, , and . Watch this short video for more on SCOT: http://bit.ly/amazon-scot
Within SCOT, Amazon's Fulfillment Network Planning (FNP) team focuses on generating free cash flow and fast customer deliveries by designing Amazon's near-term network. Amazon's network involves thousands of trucks and dozens of planes every day to move items that customers have ordered from our Fulfillment Centers to our Sort Centers and Delivery Stations. A quality network design saves Amazon tens of millions of dollars a year, improves the promised delivery time we can offer our customers, and reduces the possibility of missing a promised delivery date.
FNP is building the software solution to automate the Network design of the outbound network. Automation saves time and serves as a foundational requirement for global network across tens of thousands of configurations with trillions of potential combinations.
· A MS in CS, Operations research, or in a highly quantitative field. · 3+ years of hands-on experience (academic or industrial) in modeling and large data analysis. · Strong coding and problem-solving skills in at least one programming such as Python, Java, C++, etc. · Working knowledge of web-scale data (e.g., Hadoop). · At least one publication, as first author, in a leading conference or journal related to computer science. · Sound theoretical understanding of broad concepts, with and demonstrable expertise.
· A PhD in CS, Operation research, or in a highly quantitative field. · Prior work experience as an applied scientist or a data scientist at a consumer product company. · Experience using an object-oriented to write production-ready code. · Strong record of publications in one of the following areas: information retrieval, , recommender systems, reinforcement , multi-armed bandits. · Industry experience working with search engines, autocomplete, or recommender system · Ability to work on a diverse team or with a diverse range of coworkers
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