Senior Data Scientist, Credit Modeling

Pay Pay is a Fin Tech company that has grown to over 75M (as of August 2026) users since its launch in 2018. Our team is hugely diverse with members from over 50 different countries.

We dare to believe that we do not need a clear vision to create a future beyond our imagination. Pay Pay will always stay true to our roots and realize a vision (future) that no one else can imagine by constantly taking risks and challenging ourselves. With this mindset, you will be presented with new and exciting opportunities on a daily basis and have the opportunity to grow and reach new dimensions that you could never have imagined.

We are looking for people who can embrace this challenge, refresh the product at breakneck speed and promote Pay Pay with professionalism and passion.

Please note that you cannot apply or be selected in parallel with Pay Pay Corporation, Pay Pay Card Corporation and Pay Pay Securities Corporation.

As a Senior Data Scientist in Pay Pay 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 Pay Pay.

• 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 the 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.

• 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.

• 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,…