Data Scientist & Machine Learning Engineer building production-grade predictive pipelines and autonomous AI agent architectures. 10+ shipped projects spanning dual-model platforms, Explainable AI (SHAP), and asynchronous execution runtimes.
An end-to-end dual-model financial risk application combining freight cost regression (R² = 96.99%) and an invoice risk classification engine (94% accuracy). Features automated hyperparameter optimization via GridSearchCV, an embedded SQLite relational schema for sub-second queries, and a live Streamlit interface for finance teams.
An autonomous conversational mentor developed during the Microsoft Frontier Agent-a-Thon. Built using Python, the GitHub Copilot SDK, and AsyncIO runtime loops. Features dynamic tool routing, customized career roadmapping, technical interview scenario generation, and persistent multi-turn session memory deployed on Streamlit Cloud.
Benchmarked Random Forest against Logistic Regression classifiers for bank term deposits, applying SHAP values to explain feature contributions (call duration, account balance, age) to financial decision-makers.
Engineered an unsupervised customer clustering pipeline using PCA dimensionality reduction and K-Means to identify and visualize 5 actionable demographic and spending profiles.
Interactive business intelligence report analyzing transaction patterns, profit margins, and cohort performance designed for non-technical executive decision-making.
Normalized relational schema with 500+ records demonstrating window functions, CTEs, running revenue totals, and complex joins for revenue trends and inventory audits.
Core coursework: Data Structures & Algorithms, Object-Oriented Programming, Database Systems, Artificial Intelligence, and Software Engineering.
Actively interviewing for Junior Data Scientist and Machine Learning Engineering positions. Available for remote roles or on-site opportunities.