Senior Data Scientist, Credit Modeling

Job Description

As a Senior Data Scientist in PayPay Credit Modeling, you will lead work that improves credit risk and financial decisions.

You will lead modeling initiatives from identifying opportunities and framing ambiguous business problems through developing models, designing credible evaluations, and assessing real-world impact. You will collaborate closely with product, risk, data engineering, and machine learning engineering teams to translate research into reliable, scalable solutions.

This is a role with scope to shape the technical direction of modeling and analysis, coach colleagues, and influence decisions across PayPay.

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.

Qualifications

• 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

• 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.

Ways of working

• Take ownership of ambiguous questions and bring structure to the work needed to answer them.
• Challenge assumptions constructively and revise your view when the evidence changes.
• Balance speed with rigor, explainability, and the ability to maintain what you build.
• Communicate trade-offs and decisions clearly to partners with different expertise and priorities.