データサイエンティスト （与信領域） at PayPay Card

PayPay Card is seeking a Data Scientist to join their team in Tokyo or Fukuoka. This role is within the Credit Risk domain, focusing on leveraging data and digital technologies to transform services, business, and work practices. The company was established in 2021 to provide more user-friendly and convenient fintech services by merging with PayPay's payment platform.

The Data Scientist will be part of the Screening Planning Department, specifically the Screening Analysis Section, which consists of 10 members. The mission is to conduct advanced analysis using AI and machine learning, applying the analysis results to business operations. The main areas of focus will be screening and debt collection, identifying and resolving issues within the department.

PayPay card is moving toward becoming an overwhelmingly No.1 service. The ideal candidate will collaborate with PayPay members and internal stakeholders, working on multiple projects related to the credit risk domain. They should have experience in using statistical and machine learning techniques, and be able to apply the results to business operations.

Responsibilities:

• Conducting advanced data analysis using statistical and machine learning techniques in the credit risk domain.
• Applying analysis results to business operations.
• Collaborating with PayPay members and internal stakeholders.
• Identifying and resolving issues within the screening and debt collection areas.

Requirements:

• Practical experience in programming (Python or SQL).
• Experience in advanced analysis using machine learning and applying the results to business operations, OR experience as a consultant using machine learning for problem-solving.

The role offers:

• Opportunity to work on a large scale, with over 63 million PayPay users.
• Ability to directly contribute to the company's profits and experience exciting projects.
• Usage of machine learning tools like DataRobot and DWH like GCP, adopting automation and accuracy improvement technologies.
• Flexible work style with remote work options available.