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
· Bachelor's degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with 5 years of working experience as a Data Scientist · Experienced in writing academic-styled papers for presenting both the methodologies used and results for data science projects.· Master's degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with at least 2 years of working experience as a Data Scientist. · Proficient with data analysis and modeling software such as Spark, R, etc. · Proficient with using scripting language such as