Currently an Extern @Breaking GamesI am a University of Washington junior focused on Data Analytics, sharpening my resume through an SQL and database architecture externship with Breaking Games.
Portfolio of SQL queries, schema work, and database architecture tasks completed during my Breaking Games externship and related coursework at the University of Washington.
Designed a responsive web app connecting surplus restaurant food to low-income Seattle neighborhoods, integrating real-time surplus tracking and pickup scheduling. Piloted with 10+ restaurants to reduce estimated food waste by 15%; conducted user research to refine features like pickup time filters and one-tap reservations.
Built ML classification models (logistic regression, decision tree) achieving 85% accuracy in predicting wildfire risk. Automated the EDA workflow and feature validation using bootstrap testing (95% CI) for more robust model evaluation. Translated complex risk model outputs into stakeholder-ready visualizations in Power BI, enabling non-technical audiences to understand risk patterns and support data-informed decision-making.
I am a University of Washington junior focused on Data Analytics, sharpening my resume through an SQL and database architecture externship with Breaking Games.
I am Harshita Singh, a junior at the University of Washington pursuing a bachelor's degree and focusing on Data Analytics. I have limited work experience and am building practical skills through an SQL and database architecture externship with Breaking Games to strengthen my resume.
Externships
SQL & Database Architecture Externship with Breaking Games
Breaking Games
Experience
Software Development & Data Analytics Intern
Peace Works · Jun 2026 – Aug 2026
Data Consultant
Good Work Hub · Nov 2024 – Jan 2025
Outreach Analytics Intern
Young & Co · Jul 2024 – Sep 2024
Education
University of Washington
B.S. Informatics (Data Science) · Class of 2027
Skills
Designed a responsive web app connecting surplus restaurant food to low-income Seattle neighborhoods, integrating real-time surplus tracking and pickup scheduling. Piloted with 10+ restaurants to reduce estimated food waste by 15%; conducted user research to refine features like pickup time filters and one-tap reservations.
Built ML classification models (logistic regression, decision tree) achieving 85% accuracy in predicting wildfire risk. Automated the EDA workflow and feature validation using bootstrap testing (95% CI) for more robust model evaluation. Translated complex risk model outputs into stakeholder-ready visualizations in Power BI, enabling non-technical audiences to understand risk patterns and support data-informed decision-making.