• 3+ years of building machine learning models for business application experience
• Ph D, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Amazon Japan Store Tech (JST) Science team serves as the core science division of JP Store Tech, with the vision to enable and accelerate the best-in-class CX through state-of-the-art machine learning technologies. This team owns the science vision definition, science roadmap planning, and science solution delivery in key business areas in Japan including Search, Everyday Essential, Customer Growth and Engagement, Personalization and Delivery.

As an Applied Scientist, you will work on the design, implement and deliver models on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.

Key job responsibilities

• Initiate and drive the design and delivery of science solutions or systems that address significantly large or endemic customer and business problems, and directly impact the goals of your entire organization, and possibly others.
• Analyzing data and metrics relevant to the customer experiences.
• Recognized by internal and external peers as a thought leader in your area(s) of scientific expertise. You publish your results at top academic conferences and engage with the academic community.

Your benefits include:

• Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers
• The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact
• Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, Mx Net, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.