At Pepper Pay, I build production machine learning solutions for transaction analytics, operational forecasting, and customer behavior analysis across high-volume fintech systems.
I develop scalable Python, TensorFlow, PySpark, and SQL pipelines for feature engineering, training, validation, and batch inference on multi-million-record datasets. My work improved forecasting performance by approximately 21% and reduced data-preparation runtime by more than 35%.
Previously, I built predictive analytics, reporting automation, dashboards, and ETL workflows at Standley Systems, following earlier data analytics and software development work. I also mentor junior engineers and help teams standardize model deployment, monitoring, and production support.
