A fulfillment center can appear to be a chaotic place. With millions of items moving in and out of an area twice as large as your average theme park every day, a quick peek under the roof would reveal a manic scene. You would see associates, machinery, vehicles, robots, and packages flying about at break-neck speeds and accessing a breathtaking number of shelves and bins that store products. In reality, however, this activity is all highly organized and orchestrated by software, with each movement carefully planned and optimized. However, there are times when mistakes occur: products may disappear, reappear in strange places, become damaged, or worse. When that happens, how does a fulfillment center operating at that size, speed, and scale recover gracefully and with minimal cost? How do we ensure that we meet our commitments to our customers and merchants? How do we find a needle in a haystack when we’re constantly shuffling the haystack around? And how do we do it at extreme scale in
· Masters or PhD in Computer Science, Statistics, Operations Research or a related quantitative field · · 7+ years of hands-on experience in Applied machine learning, Predictive modeling and analysis · · Strong algorithm development experience · · · Skills with Python, R or other scripting language, as well as Java, C++, or other programming language · · Demonstrated ability to effectively deal with ambiguity and deliver results · · Strong communication and data presentation skills · Experience leading a science team, establishing direction and identifying the appropriate application of mathematical