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.


1 Result
Research Scientist, Customer Program
location Tokyo
Customer Program
Full Time

The Customer Program, one of our core sciences programs, builds models for customer understanding and applying them to customer targeting solutions. We are working on the following research topics in the Customer Program at our Tokyo office:

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.

Social Graph: Rakuten has already been applying various kinds of marketing measures based on customer profiling, target prospecting algorithms. Those models were built with individual customer behavior data. However, if we aware of social relationship between Rakuten members, such as family, colleges, friends, it is possible to improve performance of the marketing measures. With this assumption, research scientists builds the social graph structure among Rakuten members and their relatives by collecting data from multiple Rakuten services, and apply graph algorithms including graph neural networks to conduct marketing measures, such as family introduction campaign, based on the social graph.

Geo Science: With more than 70 different services in the Rakuten Group globally, our data science teams cover diverse initiatives in both the online and offline worlds. Offline Geo data and Geo Science applications are key to the optimization and overall success in fields including Logistics, Site Planning, Area Marketing and improving Customer Experience. The Geo Science Team creates algorithms and builds new Geo Intelligence layers to better understand offline activity to support many Rakuten Group services.

Marketing 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. To deliver right contents at the right time to the right customers, research scientist applies various kinds of machine learning algorithms such as reinforcement learning, deep learning to build models to marketing platforms.

Federated Learning: Rakuten group is conglomerate.  As each group company has its own privacy policy, detailed user behavior data cannot be shared with each other even if they are within Rakuten Group. Federated learning scheme is required in order to build customer models with fully utilizing Rakuten group data and complying privacy policies.

Fintech Optimization: Rakuten Securities is one of the top asset management platforms in Japan. Our scientists work closely with foreign exchange dealers and stock traders from Rakuten Securities to develop algorithms that competitively price assets based on real-time customer transactions and market conditions. We are working on challenging problems including pricing and user demand forecasting, and pricing optimization for services including foreign exchange and stock lending, using various machine learning algorithms.

We are looking for research scientists with a strong research background in user behavior, machine learning, and a strong desire to join our team.