About

About Me

My name is Phillip Vargas, and I am a data scientist with a background in healthcare corporate accounting and entertainment marketing analytics. I gained valuable experience at DaVita Medical Group and Kaiser Permanente, where I built a strong foundation in financial analysis, budgeting, and forecasting. During my tenure, I streamlined physician bonus payouts and optimized pharmacy audit processes using Excel, SQL, and data automation techniques. These efforts delivered impactful insights and significantly improved operational efficiency.

To enhance my technical expertise, I obtained a Master's Degree in Data Science with a specialization in AI, Optimization, and Machine Learning from National University, earned a Galvanize Data Science Immersive Certification, and became an AWS Certified Cloud Practitioner. This formal training equipped me with advanced skills in Python, SQL, big data management, and machine learning.

With this advanced training, I transitioned to a role as a Data Analyst at AEG Presents. In this position, I consolidated data sources from various digital marketing platforms (Meta, Google Ads, TikTok, X, etc) to develop self-service dashboards, enhancing reporting efficiency by 40%. I led initiatives to improve conversion attribution accuracy for online sales and designed an ETL process for a pilot subscription pass program. My diverse skill set in accounting, marketing, and data science enables me to identify strategic opportunities and drive growth through data-driven solutions.

Projects

Project 1
Enhance Netflix Engagement Data using LLMs

Enhanced Netflix’s engagement data usability by enriching 18,000 records using the LangChain framework and OpenAI ChatGPT API.

Github
Project 2
Probability Distribution Image Classifier

Created a probability distribution auto detection tool by classifying images using Convolutional Neural Networks (CNNs), improving analysis speed for data professionals.

Github
Project 3
Van Rental Sales Forecasting

Optimized vehicle fleet inventory by predicting van rental sales demand using time series analysis models.

Github

Skills

Python
Python

I became highly proficient in Python during the Galvanize/Hack Reactor Data Science Immersive program, where I received intensive 40-hour-per-week training for 12 weeks from experienced data scientists.

SQL
SQL

My formal training programs provided me with foundational SQL skills. By consistently applying these skills to large-scale projects as a Data Analyst at AEG, I developed advanced SQL expertise.

Data Cleaning
Data Cleaning

With a strong background in cleaning large volumes of real-world data, I consistently focus on delivering business value. My experience includes processing financial datasets for automated bonus payouts and refining digital marketing data for the development of self-service dashboards.

Machine Learning
Machine Learning

Applying machine learning in real-world scenarios, I have utilized supervised learning to enhance large company datasets and unsupervised learning to cluster unstructured data. By carefully weighing interpretability versus complexity in algorithm selection, I have effectively provided business value to various organizations.

Contact Me

DataScientistPhil@gmail.com

(909) 319-1953

LinkedIn
GitHub