YE JI JEON

LOS ANGELES,CA

Summary

Highly-motivated employee with desire to take on new challenges. Strong work ethic, adaptability, and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills.

Overview

years of professional experience

Work History

Data Engineering Researcher

Seoul National University

Seoul, South Korea

10.2020 - 07.2022

Utilized my expertise as a data engineer researcher in a food engineering laboratory to design and implement Python algorithms for extracting valuable information from social media networks like Twitter and Reddit.
Crawled and analyzed raw data from web sites utilizing API and NLP skill sets, incorporating techniques like embedding, LDA analysis, K-means clustering, and sentiment analysis.
Applied and integrated machine learning techniques along with advanced NLP skills such as nltk, gensim, and spacy.

Data Entry Clerk

Iggee inc

Buena Park, CA

12.2020 - 10.2021

Demonstrated skills in both data entry and customer service roles.
Managed account on eBay, ensuring top seller status by delivering exceptional customer service and prompt item delivery.
Identified areas for improvement by regularly reviewing feedback.
Monitored competitors and made necessary adjustments to prices, shipping, and promotions.

Education

Bachelor of Arts - Data Science

University of California, Berkeley

Berkeley, CA

08.2020

Skills

Python
SQL
Java

Numpy
Pandas
R studio

Personal Information

Nationality: South Korean

Projects

Git-let: Implementing version-control system that mimics some of the basic feature of the popular system Git which uses init, add, commit, rm, log, status, global-log, branch, rm-branch, merge and reset.

The Enigma: Created a working machine (which was used during World War II to encrypt its military communications by Germany) in Java, consisting the same features as the original enigma machine using data structures including rotor, the reflector, and the plug-board.

Predicting Taxi Ride Duration: Created a regression model that predicts the travel time of taxi ride in NY using data selection, cleaning, EDA, feature engineering, and model selection. Also used sklearn to process data and fit linear regression model.

Ham/Spam Classification: Created a classifier that can distinguish spam (junk or commercial or bulk) emails from ham (non-spam) emails using seaborn, numpy, pandas and matplotlib.

Maps: Created a visualization of restaurant ratings with machine learning and the Yelp academic dataset using K means, unsupervised/supervised learning algorithm, and simple least square linear regression to develop a model.

Timeline