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.
PaypayAs 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 include owning the scientific approach to modeling work, from defining the decision and expected value through baselines, validation, and a clear recommendation; developing and evaluating models and features using large, varied datasets and choosing methods that fit the problem and can be explained, validated, and maintained; setting evaluation standards for how models will be used, challenging assumptions, detecting leakage and selection bias, accounting for delayed outcomes, and using appropriate baselines, time-aware tests, and calibration to judge whether results are reliable; estimating likely effects on revenue, credit losses, exposure, portfolio quality, and customer outcomes, and making assumptions and uncertainty clear; guiding promising models through production with data and machine learning engineers, defining model and feature requirements, validating deployed behavior, monitoring performance and outcomes, and recommending changes based on evidence; communicating findings and trade-offs to technical and non-technical stakeholders and helping teams decide whether to proceed, change course, or stop; and leading technical discussions, critically reviewing modeling approaches, mentoring other data scientists, and establishing reusable practices that improve the team's work. Ways of working include taking ownership of ambiguous questions and bringing structure to the work needed to answer them; challenging assumptions constructively and revising your view when the evidence changes; balancing speed with rigor, explainability, and the ability to maintain what you build; and communicating trade-offs and decisions clearly to partners with different expertise and priorities.
- 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. - 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. - 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. - 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, 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.
- Social Insurance (health insurance, employee pension, employment insurance and compensation insurance) - 401K - Translation/Interpretation support - VISA sponsor + Relocation support - Annual leave (up to 14 days in the first year, granted proportionally according to the month of employment. Can be used from the date of hire) - Personal leave (5 days each year, granted proportionally according to the month of employment) - PayPay's own special paid leave system, which can be used to attend to illnesses, injuries, hospital visits, etc., of the employee, family members, pets, etc.
