Jobs in Japan
Explore hand-picked jobs in Japan for English speakers across tech, education, marketing, and more.
Vetted companies only. Apply from overseas.
Jobs in Japan
Explore hand-picked jobs in Japan for English speakers across tech, education, marketing, and more.
Vetted companies only. Apply from overseas.
PaypayThis job posting was originally in Japanese. The English version is automatically translated. Please visit the company site for the most accurate information.
PayPay’s Financial Business Development Division is responsible for planning and executing new financial businesses to evolve PayPay from a payment app into a “financial super app.” Focusing on three areas—Web3, global expansion (international alliances), and AI/data utilization—the division promotes product development using partnerships with leading players in Japan and overseas, as well as cutting-edge technologies. Building on a user base of more than 70 million, it aims to create advanced, smartphone-centric financial experiences on a global scale. PayPay aims to become the No. 1 fintech company and bring new ways of thinking about finance to the world. AI and data utilization are central to business growth and product evolution. We are implementing AI and machine learning technologies in real-world services, using the large volumes of data generated by our payment and financial services to provide more convenient and secure financial experiences for users and merchants. In the financial services domain for users and merchants, the team uses data for risk assessment and product improvement. It has designed and developed the data utilization and machine learning systems that underpin core financial services for merchants, including PayPay Funding and PayPay Merchant Loans. Going forward, we will continue creating new financial experiences across a range of services by leveraging AI and data technologies. PayPay operates in mission-critical areas directly involving money and user data. In addition to meeting business requirements and delivering high performance, machine learning models must be reliable, explainable, stable, secure, and continuously improved. Raising AI systems, which inherently involve uncertainty, to a quality level suitable for safe use in financial services is a significant challenge. The team works closely with business teams and takes end-to-end responsibility, from defining problems through designing and implementing ML systems to operating them in production. We go beyond model development and take ownership within our team of the architecture, data quality, monitoring, and operational improvements needed to make these systems work as part of products and business processes. We welcome people who want to understand business challenges and deliver machine learning as real product value to society, rather than limiting their work to research or proof-of-concept projects. Experience in the financial domain is not required. In PayPay’s financial business areas—including credit, banking, insurance, and asset management—you will lead the development of credit scoring, risk management, and marketing models, as well as the design of system architectures for AI utilization. Drawing on deep expertise in mathematical engineering and statistics, you will envision and design the overall structure of optimal machine learning systems that meet business requirements such as explainability, stability, and cost. As a tech lead, you will drive real-world implementation in collaboration with in-house engineers and data scientists. Your responsibilities will include designing and developing key models in the financial domain; defining business requirements at the outset of projects; selecting and designing predictive models that account for data bias and business constraints; and defining quality requirements and designing architectures for machine learning systems. You will also help shape the development culture by working with engineers, data scientists, and data analysts to determine development approaches for productization, conduct code reviews, and advance testing strategies. You will update the architecture of the mechanisms for model training, evaluation, monitoring, and continuous improvement (MLOps) across PayPay’s financial businesses and drive operational improvements. You will have the opportunity to develop services that provide better financial experiences to more than 70 million users and merchants across Japan. You can work with data that only you have access to and business challenges that only your team can experience, while playing a central role in growing the organization’s use of AI. Although you will belong to a business development division, you will work closely with colleagues who have diverse areas of expertise and with members of other departments, broadening your communication skills and perspective.
- Deep understanding of the mathematical theory and algorithms in fields such as machine learning, data mining, and statistics, with the ability to design approaches from scratch. This may be demonstrated by publications accepted or presented at leading ML/AI conferences or journals, or an equivalent advanced mathematical background. - At least approximately three years of practical experience developing predictive models using machine learning, including developing and operating models with real-world data in domains such as credit scoring, risk management, fraud detection, marketing optimization, or user behavior prediction. - Experience defining system quality requirements, such as predictive accuracy, explainability, stability, and cost, based on business requirements. - Experience leading the design of an overall system structure for safely integrating models into products or business processes, such as conceptualizing and designing ML pipelines or MLOps. - Experience developing and leading software as part of a team using Python or similar technologies. Experience proactively driving code reviews and quality improvements using package management, testing tools, linters, and other tools.
- Practical experience in finance or payments, such as developing credit or risk management models at a fintech company, bank, securities firm, or credit card company. - A Ph.D. in mathematical engineering, statistics, computer science, or a related field. - A record of high rankings in data analysis competitions such as Kaggle, including Kaggle Master or Grandmaster status. - Experience building and operating data processing pipelines using cloud environments such as GCP or AWS to handle large datasets ranging from tens of millions to hundreds of millions of records. - Experience implementing and operating MLOps platforms such as Kubeflow or MLflow to ensure the stable and efficient operation of machine learning systems. - Project management experience as a team lead or tech lead for a team of approximately 5–10 people. - Contributions to open-source software communities or experience reviewing papers for major conferences in Japan or overseas. - Business-level English proficiency, such as a TOEIC score of 800 or higher or experience participating in technical discussions in English.
- Social insurance coverage, including health insurance, employees’ pension insurance, employment insurance, and workers’ compensation insurance. - Corporate defined contribution pension plan. - Annual paid leave: 14 days in the first year, prorated according to the month of hire. Leave may be used from the date of hire. - Personal leave: five days granted each fiscal year; three or five days granted in the first year depending on the month of hire. This special paid leave may be used for illness or injury affecting you, a family member, or a pet, as well as accompanying someone to medical appointments.
