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, Customer Growth and Engagement, Personalization and Delivery.

As an Applied Scientist, you will 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. You will then integrate these models into the production system that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.

• Key job responsibilities
• Invent or adapt new scientific approaches, models or algorithms inspired and driven by customers’ needs and benefits at the project level.
• Analyze data and identify the gaps in existing solutions, and propose innovative science solutions.
• Contribute to research papers that are published at peer-reviewed internal and/or external venues, and contribute to the wider scientific community.
• Working with teams worldwide on global projects.

Basic Qualifications

• PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
• 3+ years of building models for business application experience
• Experience programming in Java, C++, Python or related language

Preferred Qualifications

• Experience in solving business problems through machine learning, data mining and statistical algorithms
• Experience with popular deep learning frameworks such as Mx Net and Tensor Flow
• Experience implementing algorithms using both toolkits and self-developed code

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