Currently an Extern @Wayfair
I’m passionate about using AI, data science, Python, R, SQL, and analytics to turn complex business data into actionable insights that improve marketing, product performance, and revenue growth.
My portfolio showcases projects from my externship at HP Tech Ventures, where I analyze and evaluate emerging tech startups.
Breaking Games · Aug 2026 · ✅ Verified by Extern
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
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Wayfair · ✅ Verified by Extern · ⏱️ In progress
Learn one of the most in-demand skills shaping the future of work: building AI agents. In this externship, you’ll step into the role of an AI Agent Developer and use n8n to design agents that think, plan, and act on real data — automating insights, decisions, and workflows. No coding experience required. By the end, you’ll create a live dashboard powered by your own AI agents, gaining hands-on experience that applies to any role or industry — from marketing and consulting to product management and data science.
I’m passionate about using AI, data science, Python, R, SQL, and analytics to turn complex business data into actionable insights that improve marketing, product performance, and revenue growth.
Business analytics professional using AI, data science, Python, R, SQL, Power BI, and Databricks to transform complex data into actionable insights. Through my Breaking Games externship, I analyzed e-commerce, marketing, and product performance to support smarter decisions and business growth.
Externships
Wayfair n8n AI Agent Engineering Externship
In progress
Experience
Pricebook Associate
7-Eleven · Dec 2023 - May 2026
Education
East Texas A&M University
M.S in Business Analytics · Class of 2025
Skills
✅ Verified by Extern
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
The work converted six raw CSVs into a populated SQLite analytics database, cataloging sources and capturing five immediate answers in a Holiday Game Plan. The contributor reconciled four disjoint tables into a dim_product table with stable IDs, joined sessions, carts, and sales, and attributed revenue to Meta campaigns. The output included a Q4 ad allocation plan and a clean_checkouts view that
I built a populated SQLite database from six CSV sources, authored SQL that returned five direct answers (top products by units and revenue, catalog size, ad spend, checkout sessions), created a dim_product table with stable IDs and join logic, delivered a campaign-level Q4 ad allocation plan, and produced a clean_checkouts view that excluded bot traffic.
✅ Verified by Extern · ⏱️ In progress
Learn one of the most in-demand skills shaping the future of work: building AI agents. In this externship, you’ll step into the role of an AI Agent Developer and use n8n to design agents that think, plan, and act on real data — automating insights, decisions, and workflows. No coding experience required. By the end, you’ll create a live dashboard powered by your own AI agents, gaining hands-on experience that applies to any role or industry — from marketing and consulting to product management and data science.
The externship trained participants to design AI agents with n8n that can integrate data and automate workflows. The project work covered installing and exploring the n8n platform and configuring agents to interact with data and external services. Deliverables included a live dashboard driven by the agents.
I built a Moodboard Generator agent in n8n that converted interior design style briefs into detailed image prompts and documented how prompt specificity affected moodboard layouts.
During the n8n prompt node task I converted style briefs into detailed image prompts, documenting example prompts for living rooms and bedrooms and noting how increased specificity improved the moodboard layout outputs.
I built an n8n workflow that routed user input, fetched Amazon and social data, ran AI classification and image generation, and produced a packaged Area Rug Trend Report (PDF) and demo video.
The project parsed chat input to detect rug categories, fetched product lists from Wayfair, IKEA, Amazon and Walmart, merged structured product data, and generated a PDF competitor analysis report with pricing and positioning observations.