I developed 4 machine learning projects as part of a full-time immersive data science program. The projects focused on various applications and techniques, including: NLP, Topic Modeling, Classification and Linear Regression Models, and XGBoost.Examples include:1. Unsupervised NLP topic-modeling using LDA on over 14,000 articles from 15 news publications. Further examined the significance of different article topic distributions with multiple hypothesis A/B testing and examined publication readability levels using spaCy.2. A recommender system for video games modeled using an Alternating Least Squares collaborative filtering approach with PySpark. Over 300 millions rows of data were hosted on a MySQL server on an Amazon RDS instance for use in the project. The project was visualized using a Flask app that generated recommendations on-demand.3. Supervised classification model using XGBoost to predict poverty rate in the US by state with over 70% precision on the national level. Explored features relating to poverty on the national and state level with visualization of important drivers in Tableau Online.4. Supervised linear regression to predict laptop prices for top-selling laptops on Newegg.com. Scraped data using BeautifulSoup and Selenium and explored multiple drivers for laptop pricing based on brand, screen size, and other hardware specifications.

- Led financial audit engagements for public and private companies with US and international operations in the semiconductor, software, and agriculture industries under new accounting standards.- Provided technical accounting consultations and research as a member of the Audit Quality Network.- Co-founded the Bay Area Excel Group, designing and providing training and resources for Microsoft Excel usage for employees in the Bay Area.

- Organized expenses and conducted audits for monthly expenses.- Analyzed data with Google Analytics and Google AdWords to identify website trends.- Created Excel macros for automated report organization.