I’m on a journey to transform data into actionable insights, enhancing brand strategies and consumer engagement.
Explore my projects, where I apply data analytics to uncover insights and drive strategic decisions in consumer behavior.
Beats By Dre · Sep 2026 · ✅ Verified by Extern
A Beats by Dre externship in data analytics, focused on extracting consumer sentiment and combining qualitative and quantitative methods to surface product-related insights.
Cleaned and processed raw U.S. election data (year, state, party, votes) in Python using string manipulation and manual data handling to calculate state-level wins and vote share by party, including tie handling across multiple election years; tracked key state-level outcomes and visualized voting trends and vote percentage shifts over time using Matplotlib
I’m on a journey to transform data into actionable insights, enhancing brand strategies and consumer engagement.
I am a junior at the University of Maryland pursuing a Bachelor's Degree. While I have some work experience, I’m eager to officially kickstart my career in data analytics and consumer behavior. I'm passionate about uncovering market trends and utilizing insights to drive business strategy.
Externships
Beats by Dre Data Analytics: Qualitative & Quantitative Insights Externship
Beats By Dre
Experience
Extern
Beats by Dre · Jun 2026 - Aug 2026
Data Center Site Selection Analytics -- Tableau
Intel (Sustainability Team) · Jul 2026 - Aug 2026
GRAMMY Awards Website Analytics & A/B Testing -- Excel
The Recording Academy · Jun 2026 - Jul 2026
SI PASS Leader
University of Maryland - Baltimore County · Aug 2024 - May 2025
Education
University of Maryland - College Park
Bachelor's, Information Science
Skills
✅ Verified by Extern
A Beats by Dre externship in data analytics, focused on extracting consumer sentiment and combining qualitative and quantitative methods to surface product-related insights.
The work analyzed retailer reviews and 5,110 survey responses to identify recurring product issues and map buyer behavior. It produced a pain-point chart based on sampled retailer reviews, a spreadsheet customer journey mapped to AIDA stages, descriptive and correlation analyses of survey data, thematic coding of open responses, and charts plus an AI-informed analysis plan.
I produced a pain-point chart that identified recurring issues (comfort/fit, price sensitivity, durability, connectivity, ANC expectations) and analyzed 5,110 survey responses, coding open answers into themes and delivering charts plus an AI-informed analysis plan.
Cleaned and processed raw U.S. election data (year, state, party, votes) in Python using string manipulation and manual data handling to calculate state-level wins and vote share by party, including tie handling across multiple election years; tracked key state-level outcomes and visualized voting trends and vote percentage shifts over time using Matplotlib
Contributed to designing a normalized 5-entity relational schema and wrote 6 SQL views using joins, HAVING, and nested subqueries to surface violation trends, officer breakdowns, and payment/enforcement patterns; helped reason through data ethics considerations in schema and query design, including PII exclusion and geographic bias risk in zip-code-level citation data