Research Scientist Amazon
Seattle, WA

Job Description

Amazon Fulfillment Services is looking for a motivated individual with strong analytical skills and practical experience to join our Modeling and Optimization team. We are hiring specialists at all levels into our scientific team with expertise in machine learning, network and combinatorial optimization, algorithm design, and/or control theory.

Amazon.com has an annual revenue of $233B and is growing rapidly. Because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of analytical strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of the North American, European, and Japanese fulfillment networks as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving the fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools.

The ideal candidate will have the following skills:

* Ph.D. in Operations Research, Computer Science, Applied Mathematics or a related field with publications in refereed academic journals.
* Good communication skills with both technical and business people. Ability to speak at a level appropriate for the audience.
* Experience writing scripts (Perl, Python, Ruby, Groovy) to manipulate data and developing software in traditional programming languages (C++, Java).
* Experience designing simulation and optimization models in for business decisions (e.g., staff/job scheduling, network routing, facility location) and/or feedback and/or predictive control.
* Experience in designing/implementing online algorithms and approximation schemes for hard optimization problems (e.g. dynamic resource allocation problems for fulfillment center processes).
* Experience designing/implementing machine learning algorithms tailored to particular business needs and tested on large datasets.
* The ability to implement models and tools through the use of high-level modeling languages (e.g. AMPL, Mosel, R, Matlab, Julia) is a plus.