Aspiring Data Scientist and Analytics Professional

Nayeong Kang

312-***-**** Chicago, Illinois ***** *******@***.*** www.linkedin.com/in/nayeongkang1218 EDUCATION

University of Illinois Chicago – Chicago, Illinois (Expected Graduation – May 2026 ) Bachelor of Science – Information and Decision Sciences/ Minor in Accounting University of Chicago – Chicago, Illinois

Master of Science in Applied Data Science

Admitted – Program begins September 2026

RELATED EXPERIENCE

University of Illinois Chicago – Chicago, Illinois Aug 2025 - Dec 2025 Student Consultant Intern-Hollister Inc

● Built a scalable ETL pipeline in Python and PySpark to scrape and preprocess social media posts from Reddit, and Ostomy forums, converting unstructured content into structured datasets.

● Developed NLP and multinational AI workflows with Gemini API. and RoBERTa to standardize text and emotion analysis.

● Built Power BI dashboard and conducted comparative analysis to identify key drivers of customer satisfaction and dissatisfaction, benchmarking insights against competitor companies

University of Illinois Chicago – Chicago, Illinois May 2025 - Jul 2025 Research Assistant

● Analyzed survey data on user interactions with AI assistants using R; performed data cleaning, linear regression and T-test analysis, and visualization.

● Evaluated user interactions with our AI assistant system and improved AI responses and overall functionality.

● Contributed to drafting reports summarizing research findings and extracting key insights on user perceptions of AI assistants.

CBA Department of Marketing – Chicago, Illinois Sep 2024 - Present Student Teaching Aide

● Grade assignments, exams, and projects related to marketing research, and statistical tests, and metrics.

● Guide student understanding of hypothesis testing, graphs, and correlations within the context of marketing analytics.

The Global Tech Experience – Remote Aug 2023 - Dec 2023 Excel and Tableau Trainee

● Utilized Excel functions, including pivot tables and VLOOKUP, to analyze and organize large datasets. Developed a strong foundation in data cleaning and transformation.

● Enhanced business presentation skills, both written and verbal, by communicating data analysis results to both technical and non-technical stakeholders.

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