Every business generates data that holds answers to its most pressing questions: which customers are about to leave, which transactions are fraudulent, and which products each user is most likely to buy. I build custom machine learning models that turn that raw data into clear, actionable predictions so your team can act before problems happen, not after.

My process starts with understanding the business decision the model needs to support, not just the algorithm to use. From there, I handle feature engineering, model selection (XGBoost, LightGBM, Random Forest, or deep learning where it genuinely adds value), and rigorous validation so the model generalizes to real-world data, not just historical patterns. I prioritize interpretability alongside accuracy, so your team understands the reasoning behind every prediction, not just the output.

For customer retention, I identify early warning signs of churn so you can intervene before losing revenue. For fraud and risk detection, I build models that catch genuine threats while minimizing false positives, protecting both security and customer experience. For recommendation systems, I design collaborative filtering, content-based, or hybrid approaches suited to your catalog size and user behavior patterns.

Every model is delivered production-ready, with clean code, clear documentation, and a straightforward path for retraining as your data grows. This service is built for SaaS companies managing subscriber retention, fintech and e-commerce platforms handling risk, and any business looking to grow revenue through smarter, data-driven decisions.