• Selected as one of 200 members, outcompeting 1200+ applicants.

• Participated in a rigorous 10-week program of technical mentoring sessions and mock interviews.

• Engaged in 1:1 meetings with a Google Software Engineer to deepen knowledge in technical problem-solving and enhance interview skills.

• Developed SQL and Java scripts to clean 58,000+ school data entries, reducing error rate by 40%.

• Built JavaScript programs to auto-fill missing entries with data scraped from PowerSchool, cutting labor time by 81% while achieving 99% accuracy.

• Analyzed attendance data, linking transit access, and absenteeism; proposed funding to address inequities.

• Organized, filtered, and cleaned data using R programming; completed two data-cleaning projects using Kaggle datasets.

• Sourced and visualized public data, and interpreted results, completing two analytical projects.

• Cleaned and analyzed datasets containing 500+ cancer patient entries provided by Mount Sinai.