A leading fintech company in Japan providing innovative payment solutions that simplify and enhance the online and offline shopping experience.
The company offers instant, monthly-consolidated credit to consumers, using proprietary models and machine learning to underwrite transactions in real time and guarantee payments to merchants.
Its technology helps merchants increase conversion rates, reduce incomplete transactions, and encourage repeat purchases.The company continues to grow under a global payments group while maintaining its brand and commitment to convenient, data-driven financial services.
About the PositionThe Risk and Data team plays a key role in shaping the company's risk policies and vision. The team develops and deploys machine learning models that predict risk and enable real-time payment underwriting.
It consists of data scientists and data engineers building a cutting-edge risk assessment engine using a modern tech stack that includes AWS Glue, SageMaker, Apache Spark, Prefect, and Looker.
We are looking for a Data Modeling and Business Intelligence Specialist to help build data assets that represent unit economics, customer credit quality, fraud trends, and overdue debt recovery processes.
This role will directly support the development of more advanced models and policies to address key business challenges.Key Responsibilities
Data Modeling:
Design and implement data models that enhance analytics and reporting capabilities across the risk domain.
ETL Development:
Build and maintain ETL pipelines for business intelligence and machine learning applications, ensuring accuracy, scalability, and efficiency.
Analytics Enablement:
Support risk and business teams by developing flexible, insight-driven dashboards and reports using tools such as Looker.
Cross-Functional Collaboration:
Partner with data scientists, engineers, and product managers to align data initiatives with business goals and integrate new data assets and models into operations.
Skills and Requirements5+ years of experience in business intelligence or data engineering.
Strong collaboration skills, with the ability to work closely with stakeholders and deliver tangible business value.
Experience in financial services, payments, or fraud prevention is a plus (interest in these areas is essential).
Technical Expertise:
Proficiency in translating business metrics into efficient, well-documented code.
Hands-on experience with Spark (Scala preferred; PySpark acceptable with willingness to learn).
Experience with job orchestration tools such as Airflow or Prefect.
Experience designing data marts for analysts and BI tools (e.g., Looker, Tableau, Power BI).
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