Why Join
Rakuten Research

01Vast and exciting data

The Rakuten Group offers a full range of services related to daily life, from shopping and online payment to telecommunications, travel, and finance. The vast amount of diverse data we are able to generate and access makes research at RIT dynamic and exciting.

02Real business-related research

At RIT, your research will be put to real use and exposed to millions people around the world every day.

03One global team

Rakuten has six global offices, but no matter where we are in the world, we share the same challenges and successes as one team.

04Choose the best field for your personal growth

We provide an environment to learn, grow and engage with excellent researchers around the world. Receive guidance from renowned scientist and AI researchers and start exciting conversations with you peers.

CAREERS

Credit Scoring
2 Results
Lead Research Scientist, Customer Program Full time
location Tokyo
Researcher
Customer Program
Full Time

Customer Program, which is one of our core science program, aims to build universal customer models and its applications. We’re working on following four research topics in Customer Program at Tokyo office. 

Customer Pre-trained Model: Research scientists try to build universal customer models and its applications, motivated by the recent success of pre-trained models in language and vision fields. Such models can be adapted to improve diverse set of solutions for services including recommendation systems, customer targeting solutions and credit scoring functions to name a few. As initial and simpler applications, we work on predictions of customer features so-called customer DNA.

Credit Scoring: It is one of crucial research topics across Rakuten Group (Mainly in our fintech businesses such as credit card, payment, securities and banking business). Research scientist applies machine learning, data mining and optimization techniques to solve challenging problems, build models and solutions regarding credit scoring related projects. Our solution includes scoring for both business clients and individual users.

Policy Optimization: Rakuten group is contacting to our customers with various kinds of communication channels, such as showing banners on web pages, sending email, pushing notifications through apps, to promote our products and services. In order to deliver right contents at the right time to the right customers, research scientist applies reinforcement learning algorithms to build models to contents delivery platforms. 

CDNA-IA: Customer DNA (CDNA) is a platform to manage Rakuten customer profiles by utilizing data from different Rakuten businesses. CDNA-IAs, a part of CDNA, are predicted customer profiles using machine learning profiles. The team builds and operates many models for CDNA-IAs. The team does apply recent research progress to develop efficient data pipeline and model creation.

Senior Research Scientist, Customer Program Full time
location Tokyo
Researcher
Customer Program
Full Time

Customer Program, which is one of our core science program, aims to build universal customer models and its applications. We’re working on following four research topics in Customer Program at Tokyo office. 

Customer Pre-trained Model: Research scientists try to build universal customer models and its applications, motivated by the recent success of pre-trained models in language and vision fields. Such models can be adapted to improve diverse set of solutions for services including recommendation systems, customer targeting solutions and credit scoring functions to name a few. As initial and simpler applications, we work on predictions of customer features so-called customer DNA.

Credit Scoring: It is one of crucial research topics across Rakuten Group (Mainly in our fintech businesses such as credit card, payment, securities and banking business). Research scientist applies machine learning, data mining and optimization techniques to solve challenging problems, build models and solutions regarding credit scoring related projects. Our solution includes scoring for both business clients and individual users.

Policy Optimization: Rakuten group is contacting to our customers with various kinds of communication channels, such as showing banners on web pages, sending email, pushing notifications through apps, to promote our products and services. In order to deliver right contents at the right time to the right customers, research scientist applies reinforcement learning algorithms to build models to contents delivery platforms. 

CDNA-IA: Customer DNA (CDNA) is a platform to manage Rakuten customer profiles by utilizing data from different Rakuten businesses. CDNA-IAs, a part of CDNA, are predicted customer profiles using machine learning profiles. The team builds and operates many models for CDNA-IAs. The team does apply recent research progress to develop efficient data pipeline and model creation.