Data Scientist 2026 (Japan)

The Data Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, create analyses, carry over solutions from other regions, and find new ways to improve last mile analytics to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. They also work on cross-disciplinary efforts with other scientists within Amazon.
LMEA Science is requested on regular basis to conduct advanced analyses or to carry over "science" solutions built by DS I or BIE II or III from other regions. These solutions are not complex enough to justify a data scientist with a lot of experience to work on it. However, these requests are the perfect fit for a new graduate as part of their ramp up, getting use to Amazon environment and within DS I role guideline.

Key job responsibilities
Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work.

A day in the life
Come teach us a few things, and we’ll teach you a few things as we navigate the most customer-centric company on Earth.

Basic Qualifications

- Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Master's degree
- Speak, write, and read fluently in Japanese
- Speak, write, and read fluently in English
- ML Python skills (scikit-learn
- scipy, pytorch / keras / tensorflow, prophet, XGboost), Python optimization libraries (xpress)

Preferred Qualifications

- Be able to interpret coefficients from regression models in language that is not mathematical, be familiar with the various probability distributions and how to transform them into others and comfortable modelling.
- Be able to explain how things your business partners control impact whatever result they are trying to measur.