Data Scientist • Machine Learning Engineer • AI Builder

I'm passionate about using data and AI to solve real-world problems. My work spans machine learning, predictive analytics, business intelligence, NLP, LLM applications, and data engineering.

🎓 B.S. Statistics & Data Science + B.A. Economics, UCLA
🎓 Incoming M.S. in Statistics (Advanced Methods & Data Analysis), University of Washington
☁️ Former Business Intelligence Engineer Intern @ Amazon Web Services (AWS)

I enjoy building end-to-end data products—from collecting and cleaning data to training machine learning models and deploying AI-powered applications.

My interests include:

• 🤖 Machine Learning
• 📈 Predictive Analytics & Forecasting
• 🧠 Large Language Models (LLMs)
• 🔍 Retrieval-Augmented Generation (RAG)
• 📊 Business Intelligence
• ☁️ Data Engineering
• 📉 Applied Statistics

Python • SQL • R • Java • Git

Scikit-learn • CatBoost • XGBoost • Random Forests • Logistic Regression • Neural Networks • Time Series Forecasting • NLP

OpenAI API • Gemini API • LangChain • RAG • FAISS • Prompt Engineering • Agentic AI

Pandas • NumPy • Tableau • QuickSight • Redash • Jupyter Notebook

AWS • GitHub • Docker (Learning)

LLM-powered financial education assistant built using Retrieval-Augmented Generation (RAG), semantic search, and vector databases.

Tech Stack

• Python
• Gemini
• FAISS
• FastAPI
• WhatsApp API

Built machine learning models on millions of advertising interactions to predict click behavior.

Highlights:

• Feature Engineering
• CatBoost
• Logistic Regression
• CTGAN
• ROC-AUC improvement from 0.71 → 0.81

Machine learning model for automated skin lesion classification.

Topics:

• Computer Vision
• Medical AI
• Classification
• Model Evaluation

Natural Language Processing pipeline analyzing 140,000+ UFO sighting reports.

Topics:

• NLP
• Sentiment Analysis
• Poisson Regression
• Geographic Data Analysis

Forecasting macroeconomic indicators using statistical learning and time-series models.

Topics:

• Forecasting
• Time Series
• Regression
• Economics

Business Intelligence Engineer Intern

• Built forecasting pipelines
• Developed predictive models
• Automated ETL workflows
• Built QuickSight dashboards
• Improved operational planning using machine learning

Data Scientist

Built AI-powered financial education tools using LLMs, RAG, semantic search, predictive analytics, and statistical modeling.

• Production ML Systems
• MLOps
• Docker
• Kubernetes
• AWS Deployment
• LLM Evaluation
• AI Agents

• Build production-ready AI applications
• Publish more open-source projects
• Contribute to machine learning repositories
• Learn scalable ML infrastructure

"The best way to learn data science is to build things."