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DESCRIPTION

Are you interested in building state-of-the-art machine learning systems for the most complex, and fastest growing, transportation network in the world? If so, Amazon has the most exciting, and never-before-seen, challenges at this scale (including those in sustainability, e.g. how to reach net zero carbon by 2040).

Amazon’s transportation systems get millions of packages to customers worldwide faster and cheaper while providing world class customer experience – from online checkout, to shipment planning, fulfillment, and delivery. Our software systems include services that use tens of thousands of signals every second to make business decisions impacting billions of dollars a year, that integrate with a network of small and large carriers worldwide, that manage business rules for millions of unique products, and that improve experience of over hundreds of millions of online shoppers.

As part of this team you will focus on the development and research of machine learning solutions and algorithms for core planning systems, as well as for other applications within Amazon Transportation Services, and impact the future of the Amazon delivery network. Current research and areas of work within our team include machine learning forecasting, planning systems and robust decision making on large networks, as well as uncertainty quantification, generative models on graphs and ml explainability, among others.

We are looking for an Applied Scientist with a strong academic background in the areas of machine learning, time series forecasting, and/or optimization.

At Amazon, we strive to continue being the most customer-centric company on earth. To stay there and continue improving, we need exceptionally talented, bright, and driven people. If you'd like to help us build the place to find and buy anything online, and deliver in the most efficient and greenest way possible, this is your chance to make history.

About the team
The EU ATS Science and Technology (SnT) team owns scalable algorithms, models and systems that improve customer experience in middle-mile. We work backwards from Amazon's customers aiming to make transportation faster, cheaper, safer, more reliable and ecologically sustainable.