I mapped the workforce structure at an Amazon fulfillment center and documented each role's responsibilities, interactions, and tools.

I collected, cleaned, and structured employee feedback from Glassdoor and YouTube, producing analysis-ready datasets and examples of strong and weak employee voice.

I cleaned a noisy employee-feedback text column using Python, producing a consistent cleaned_text column ready for analysis.

I identified three themes from employee feedback, selected a priority theme, and ran a root-cause analysis to map underlying drivers.

I built a Prompt Node that produced detailed, reusable image prompts for a premium Organic Modern moodboard.

I delivered a final Trend Discovery Agent package including a written report, a PDF trend report, and a walkthrough video link.

I delivered a Competitor Monitoring Agent package including a written report, a PDF analysis, and a demo recording covering rug-category competitor benchmarking.

I updated an AI content agent to write Wayfair-style rug marketing, adding persona, voice rules, and tone splits between strategic and consumer copy.

I prepared a running Holiday Game Plan document and ran SQL queries against Breaking Games' SQLite data to answer week-one questions.

I designed and built a clean product dimension, joined sales and traffic tables, and produced a data-driven reorder recommendation for the holiday plan.

I wrote a Q4 ad allocation plan that connected Meta campaign spend to sales and recommended where to reallocate budget.

I wrote a diagnosis report that identified bot traffic patterns, produced a cleaned checkout view, and recalculated cart abandonment rates to separate signal from noise.