Amazon Externship

This externship project tackled understanding and improving warehouse employee experience at Amazon fulfillment centers. I conducted a multi-source sentiment analysis using publicly available data — analyzing over 500 Glassdoor reviews and 50+ YouTube employee testimonials to build a comprehensive picture of warehouse worker experience.

Tools & Methods: Built a thematic tagging system across 12 sentiment categories (physical strain, management quality, scheduling flexibility, career progression, compensation, safety, pace of work, break adequacy, seasonal pressure, peer dynamics, training quality, and turnover intent). Used Excel for data organization with pivot tables, COUNTIF-based frequency analysis, and segmentation by reviewer tenure/role/location. Applied the SCQA framework (Situation, Complication, Question, Answer) for structuring the problem narrative and the Pyramid Principle for organizing recommendations into a hierarchical argument structure. Deliverables created in PowerPoint using the Minto Pyramid and SCQA slide frameworks.

Key Insights: Across 500+ reviews, "pace of work" and "physical strain" emerged as the two highest-frequency negative themes (appearing in 68% and 54% of reviews respectively). Built root cause trees mapping burnout drivers to specific operational decisions — revealing that mandatory overtime policies during peak season were the single strongest predictor of negative sentiment. Designed friction flowcharts visualizing the employee journey from onboarding through the first 90 days, identifying three critical drop-off points. Final intervention recommendation: a real-time anonymous feedback system integrated into warehouse floor tablets, modeled on existing safety reporting infrastructure.