Wayfair Externship via Extern
I build AI agents in n8n and Python that collect market signals, analyze competitors and deliver decision-ready dashboards, most recently for Wayfair's Rugs category through Extern.
Wayfair n8n externship: four connected AI agents (trends, competitors, content) feeding one interactive market dashboard, plus independent agentic AI builds.
Wayfair (via Extern)
An executive dashboard for Wayfair's Rugs category team that unifies my Trend Discovery and Competitor Monitoring agents. It shows market size and growth, trending rug styles with colors and materials, Wayfair vs Amazon vs Walmart pricing, opportunities, risks and a prioritized action plan.
Wayfair (via Extern)
Three n8n agents for Wayfair's Rugs team. Trend Discovery turns a category into a visual market trend report. Competitor Monitoring compares Wayfair, Amazon and Walmart on price, assortment, ratings and shipping. AI Insights & Content turns both reports into an on-brand content plan.
I build AI agents in n8n and Python that collect market signals, analyze competitors and deliver decision-ready dashboards, most recently for Wayfair's Rugs category through Extern.
Before AI, I built monitoring software for a solar micro-grid serving 5,000+ cooperative members in Haiti and analyzed human rights case data. Today I'm a Machine Learning Intern at FlyRank AI and a Workflow Automation Engineering Extern with Wayfair, where I built four connected n8n agents and an executive market dashboard. I'm NVIDIA-certified in Agentic AI and Generative AI LLMs, Microsoft-certified as a Fabric Data Engineer, and completing a BSc in Computer Science at University of the People. I work in English and French.
Externships
Wayfair n8n AI Agent Engineering Externship
In progress
Experience
Machine Learning Intern
FlyRank AI · Sep 2026
Software Engineer
Coteaux District Electric Cooperative (CEAC) · Jan 2022 - Jul 2024
Human Rights Data Analyst
AfVdrab Association · Jan 2021 - Oct 2023
Education
University of the People
B.S. Computer Science (in progress) · Class of 2027
Skills
Wayfair (via Extern)
An executive dashboard for Wayfair's Rugs category team that unifies my Trend Discovery and Competitor Monitoring agents. It shows market size and growth, trending rug styles with colors and materials, Wayfair vs Amazon vs Walmart pricing, opportunities, risks and a prioritized action plan.
An executive dashboard for Wayfair's Rugs category team that unifies my Trend Discovery and Competitor Monitoring agents. It shows market size and growth, trending rug styles with colors and materials, Wayfair vs Amazon vs Walmart pricing, opportunities, risks and a prioritized action plan.
Built the n8n Dashboard Builder agent: it parses both HTML reports with rule-based JavaScript and fills every section in about 20 seconds. Fixed parsing for real report formats, then redesigned the output as an interactive page with Chart.js charts, retailer filters, light/dark themes and a section menu.
Category managers get one view instead of two long reports. Live dashboard: https://claude.ai/artifact/QMKJqsyy9otfjkn4pyNsHd | Final deck: https://docs.google.com/presentation/d/1y3Os6WC4IypsJyTl4p42uGVFFur8loFGy-1naUTUgeY/edit?usp=sharing
Wayfair (via Extern)
Three n8n agents for Wayfair's Rugs team. Trend Discovery turns a category into a visual market trend report. Competitor Monitoring compares Wayfair, Amazon and Walmart on price, assortment, ratings and shipping. AI Insights & Content turns both reports into an on-brand content plan.
Three n8n agents for Wayfair's Rugs team. Trend Discovery turns a category into a visual market trend report. Competitor Monitoring compares Wayfair, Amazon and Walmart on price, assortment, ratings and shipping. AI Insights & Content turns both reports into an on-brand content plan.
Built chat and form triggers, data fetching, multi-step LLM generation with Mistral, retry and wait logic for API limits, and HTML report assembly. In the content agent I fixed binary file reading (reports were reaching the AI empty) and added code checks that remove unsourced statistics.
Sample outputs. Trend report: https://drive.google.com/file/d/1d9bp0f8Eo3im5QJft60aYWGL5kyjlm5U/view | Competitor report: https://docs.google.com/document/d/1zXAoVy8JewAR5KS8hWJ_37JeatgsUE5rb6NLJTiAgtk/edit | Content strategy: https://drive.google.com/file/d/1JAZC1JYsmPvgIbcfTCyr-MbNUgAE7zaa/view
Wayfair (via Extern)
A 7-stage n8n workflow that turns one chat message (a rug category) into a full market trend report: input validation, data collection from Amazon, Instagram, Pinterest, blogs and market data, AI product analysis, AI mood-board images, blog insights, 7 Mistral report writers and a validated HTML out
A 7-stage n8n workflow that turns one chat message (a rug category) into a full market trend report: input validation, data collection from Amazon, Instagram, Pinterest, blogs and market data, AI product analysis, AI mood-board images, blog insights, 7 Mistral report writers and a validated HTML output.
Designed the full pipeline: Apify and HTTP data sources merged into one dataset, OpenRouter agents that remove non-rug products and identify top trends, Hugging Face image generation, Wait nodes to respect API rate limits, and a Section Validator that checks the report is complete before delivery.
Produces a decision-ready trend report in minutes instead of days. Workflow JSON: https://docs.google.com/document/d/1Ucf-IjXWyTFaFjbkl7_B2cPvIH0SH3Gs4MR0e-3aOvc/edit | Sample report: https://drive.google.com/file/d/1d9bp0f8Eo3im5QJft60aYWGL5kyjlm5U/view
✅ 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.
Built a connected market intelligence system for Wayfair's Rugs category in n8n: a Trend Discovery agent, a Competitor Monitoring agent (Wayfair vs Amazon vs Walmart), an AI Insights & Content agent, and a Dashboard Builder that turns both reports into one executive dashboard.
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Shipped 4 working agents and an interactive dashboard. Found Wayfair's $100-$250 mid-tier gap (avg $127 vs $41-$46 at competitors). Fixed empty-input and price-parsing bugs, added fact checks that remove unsourced statistics, and presented the results in a final deck and Loom demo.
During the project I drafted a detailed prompt for a moodboard agent, describing composition, textures, materials, color palette, and desired photographic style. The prompt produced a high-resolution, organized moodboard-ready spec.
I packaged four connected AI agents into a Market Intelligence demo: a Trend Discovery agent, Competitor Monitoring agent, AI Insights & Content agent, and an integrated dashboard, and submitted a slide deck, PDF report, and demo video.
The project parsed a chat input to detect rug category, fetched products from three retailers in parallel, normalized data into one schema, and ran specialized AI subagents to generate a decision-ready HTML competitor report showing pricing, positioning, and whitespace findings.
I evaluated the agent, planned and built enhancements to match Wayfair’s warm, practical voice, added audience/season/objective inputs, and implemented data-grounding and parse-error checks. The result was a working agent that outputs verified metrics and on-brand copy.