👋 I'm JUNGYUN KIM,

a data analyst turning data into

stories about people

ABOUT

Hi, I'm Jungyun Kim — a data analyst with a background in computer science. I enjoy transforming messy, real-world data into meaningful insights that help people make better decisions. Whether it's building data pipelines, visualizing trends, or exploring behavioral patterns, I aim to tell clear, impactful stories with data.

I'm currently pursing a computer science degree, building a strong foundation in data analysis, machine learning, and software development. Through my research roles at Columbia Business School and DitecT Lab, I've worked on real-world problems ranging from public transit behavior to social media engagement.

I’m currently looking for internship opportunities where I can apply my technical skills—such as Python, SQL, and data visualization—to solve real business problems and grow as an analyst.

Outside of data, I'm curious about media, behavioral science, and thoughtful design. Let's connect!

Columbia University, School of Engineering and Applied Science

B.S. in Computer Science
Exp. Graduation: May 2026
GPA: 3.8 / Dean’s List

PROJECTS

Through the Eyes of AI: Brand Perception of Robotaxis in LLM Search

Analyzing LLM-generated perceptions of top autonomous driving companies using Python to uncover AI-driven brand rankings.

NYC Congestion Pricing: How It Reshaped Subway Ridership

Analyzing NYC subway ridership trends after congestion pricing using Google Cloud Storage, BigQuery, and Tableau

EXPERIENCE

Feb 2025—Present

Data Analyst Research Assistant, DitecT Lab

Processed and analyzed ~120 million MTA subway ridership records (2021–2025) in BigQuery, optimizing SQL queries to aggregate daily, weekly, and monthly trends for congestion pricing impact analysis.
Performed cyclical detrending and time series modeling in Python to isolate post-COVID recovery effects from policy-driven changes, improving the clarity of results by removing repetitive hourly and weekly patterns.
Developed interactive Tableau dashboards to visualize ridership trends and highlight actionable insights for stakeholders, reducing manual reporting time and enhancing data-driven decision-making.

Jun 2024—Present

Research Assistant, Columbia Business School (Marketing Division)

Developed an end-to-end pipeline leveraging Python and the Meta Graph API to gather and analyze 10,000+ Facebook advertisement comments, integrating the OpenAI API for advanced NLP insights.
Automated ad deployment and setup across multiple Facebook accounts via Python scripts interfacing with the Meta Marketing API, reducing manual ad configuration time by 90%.
Performed comprehensive data analysis and visualization using Python (Pandas, Matplotlib) and R, uncovering statistically significant patterns in user engagement and political stance.

Aug 2024—Dec 2024

Teaching Assistant, CS @ Columbia University

Assisted Professor Daniel Bauer in Introduction to Python, a 300+ students class required for all engineering students.
Led weekly office hours, collaborated with 9 teaching assistants to run review sessions, graded assessments and exams.