Senior Data Scientist, Credit Modeling

**About the company:** PayPay Minato-ku, Tokyo With over 70M million registered users (as of July 2025), PayPay is the country’s most popular mobile wallet used by approximately one out of every two smartphone users in Japan. **Responsibilities:** Own the scientific approach to modeling work, from defining the decision and expected value through baselines, validation, and a clear recommendation. Develop and evaluate models and features using large, varied datasets. Choose methods that fit the problem and can be explained, validated, and maintained. Set evaluation standards for how models will be used. Challenge assumptions, detect leakage and selection bias, account for delayed outcomes, and use appropriate baselines, time-aware tests, and calibration to judge whether results are reliable. Estimate likely effects on revenue, credit losses, exposure, portfolio quality, and customer outcomes. Make assumptions and uncertainty clear. Guide promising models through production with data and machine learning engineers. Define model and feature requirements, validate deployed behavior, monitor performance and outcomes, and recommend changes based on evidence. Communicate findings and trade-offs to technical and non-technical stakeholders, and help teams decide whether to proceed, change course, or stop. Lead technical discussions, critically review modeling approaches, mentor other data scientists, and establish reusable practices that improve the team’s work. Requirements Bachelor’s Degree in a quantitative field such as Computer Science, Mathematics, Statistics, Operations Research, Machine Learning or equivalent. An advanced degree is a plus. Substantial professional experience in data science or applied machine learning, typically six years or more, with evidence of leading consequential modeling work from problem definition through production use and impact assessment. Core data science skills: Strong knowledge of statistical modeling, supervised learning, feature engineering, and model interpretation; sound judgment on when a simpler method is sufficient. Practical experience with validation and experimentation, including time-aware evaluation, calibration, bias or selection effects, and monitoring after deployment. Ability to work independently with large, imperfect datasets and distinguish a useful signal from leakage or artifacts. Financial and decision skills: Ability to connect model outputs with financial and customer outcomes, and explain the trade-offs in a decision or policy. Ability to form a recommendation when evidence is incomplete, state its limits, and identify what would change the conclusion. Core technical skills: Strong hands-on Python and SQL skills for model development, data analysis, and reproducible work. Experience using common statistical and machine learning libraries such as scikit-learn or XGBoost. Experience working with large-scale data platforms or distributed data processing to develop features, train models, and evaluate results. Experience building reproducible analysis and model workflows, collaborating with engineering teams on deployment, and evaluating models after release. Experience building point-in-time features and reproducible model workflows, and working with engineering colleagues to deploy, monitor, and improve models in production. Nice to haves While not specifically required, tell us if you have any of the following. Experience in lending, banking, payments, or another financial decision domain. Credit risk and portfolio economics experience is especially valuable. Experience applying models to financial decisions, credit strategy, or portfolio optimization. Experience with causal inference, treatment effects, decision policy evaluation, or controlled experiments. Hands-on experience with Databricks, Spark, BigQuery, or Amazon Redshift; experience with streaming tools such as Kafka or Amazon Kinesis is a plus. APPLY FOR THIS POSITION DO YOU NEED MORE INFO? ASK A QUESTION About PayPay PayPay Minato-ku, Tokyo With over 70M million registered users (as of July 2025), PayPay is the country’s most popular mobile wallet used by approximately one out of every two smartphone users in Japan. Key benefits International engineering team Incredible scale Read more about PayPay More jobs like this We'll send you a digest of new English-friendly software developer jobs in Japan. Your email stays private, we don't share or sell it. © 2026 Privacy Policy **Requirements:** Bachelor’s Degree in a quantitative field such as Computer Science, Mathematics, Statistics, Operations Research, Machine Learning or equivalent. An advanced degree is a plus. Substantial professional experience in data science or applied machine learning, typically six years or more, with evidence of leading consequential modeling work from problem definition through production use and impact assessment. Core data science skills: Strong knowledge of statistical modeling, supervised learning, feature engineering, and model interpretation; sound judgment on when a simpler method is sufficient. Practical experience with validation and experimentation, including time-aware evaluation, calibration, bias or selection effects, and monitoring after deployment. Ability to work independently with large, imperfect datasets and distinguish a useful signal from leakage or artifacts. Financial and decision skills: Ability to connect model outputs with financial and customer outcomes, and explain the trade-offs in a decision or policy. Ability to form a recommendation when evidence is incomplete, state its limits, and identify what would change the conclusion. Core technical skills: Strong hands-on Python and SQL skills for model development, data analysis, and reproducible work. Experience using common statistical and machine learning libraries such as scikit-learn or XGBoost. Experience working with large-scale data platforms or distributed data processing to develop features, train models, and evaluate results. Experience building reproducible analysis and model workflows, collaborating with engineering teams on deployment, and evaluating models after release. Experience building point-in-time features and reproducible model workflows, and working with engineering colleagues to deploy, monitor, and improve models in production. **Nice to have:** While not specifically required, tell us if you have any of the following. Experience in lending, banking, payments, or another financial decision domain. Credit risk and portfolio economics experience is especially valuable. Experience applying models to financial decisions, credit strategy, or portfolio optimization. Experience with causal inference, treatment effects, decision policy evaluation, or controlled experiments. Hands-on experience with Databricks, Spark, BigQuery, or Amazon Redshift; experience with streaming tools such as Kafka or Amazon Kinesis is a plus. APPLY FOR THIS POSITION DO YOU NEED MORE INFO? ASK A QUESTION About PayPay PayPay Minato-ku, Tokyo With over 70M million registered users (as of July 2025), PayPay is the country’s most popular mobile wallet used by approximately one out of every two smartphone users in Japan. Key benefits International engineering team Incredible scale Read more about PayPay More jobs like this We'll send you a digest of new English-friendly software developer jobs in Japan. Your email stays private, we don't share or sell it. © 2026 Privacy Policy