I turn messy datasets into intelligent systems. My focus is on Analytics, Machine Learning, and Credit Risk Modeling, backed by a strong engineering foundation in Linux, Backend, and Full-Stack Development that lets me take models from exploration all the way through to production.
Currently diving into LLM Engineering, RAG Systems, and MLOps because I want to build solutions that leverage the latest in AI.
When I'm not building, I'm writing. I share what I learn through blogs on code, technology, AI, Linux, and everything in between.
Dun and Bradstreet
Data Science Intern ( Risk Analysis and Modeling)
June 2025 – Present
As a Data Science Intern at Dun & Bradstreet, I built and validated credit risk models using logistic regression and tree based methods, and developed scorecard-style models for borrower scoring. Implemented data pipelines and feature engineering in PySpark on Databricks, ran SQL-driven analysis for data quality and exploration, and evaluated models with cross-validation and business-focused metrics. Produced actionable analytics and model artifacts to support risk assessment and decision making.
Tech Stack
If you're still reading, you're probably curious about my work.