This role requires depth in the data science fundamentals and payments domain knowledge. You should have experience with modeling, production monitoring, and experiment design, and an in-depth understanding of payment rails (ACH, RTP, card, and interchange) and payment behavior. Hands-on production ML development is essential: you have built a model, deployed it, watched it drift, and retrained it, using tooling like XGBoost and MLflow or their equivalents. You bring strong Python and SQL, plus comfort with large datasets in a distributed environment such as PySpark on Databricks, experimentation and causal inference skills, and the judgment to know which method a question calls for, as well as feature engineering instincts for transactional data. You hold a high bar for your own work: you understand your data before you draw conclusions from it, and you’d rather find the flaw in your analysis yourself.