📦 I plan demand and purchasing for 800+ SKUs at a US woodworking brand selling primarily on Amazon.
🎯 Deciding what to order is the job: forecasting, reorder points and safety stock across parent/child variations and multipack configurations (5, 8, 10, 25, 100 units) that all have to reconcile before a single PO goes out.
🌏 Negotiating with 5 manufacturers across China and Vietnam — pricing, lead times, packaging — and tracking every container with our freight forwarders until it lands in the warehouse.
⚡ Automating the analysis instead of grinding through it. A sales report that used to consume a full workday now runs in 20 minutes on the Excel/VBA systems I built. I'm extending that into n8n and AI-assisted workflows.
📉 Amazon punishes stockouts twice — you lose the sale, and you lose the ranking. Rebuilding that momentum takes months. Preventing it is what I do.
📫 Find me on LinkedIn
Amazon Demand Planning Dashboard — ▶ live demo
Inventory planning across a multipack Amazon catalog: demand forecast, reorder points, safety stock, and coverage risk measured against each SKU's own lead time. Fully synthetic data, zero dependencies, no build step.
Landed Cost & FBA Margin Calculator — ▶ live demo
What a unit actually costs by the time Amazon pays you. Freight allocated across the shipment by mode — including air's chargeable weight — plus duty, tariff, and every Amazon fee, down to net margin and break-even price.
Planning — demand forecasting · reorder points · safety stock · PO management · landed cost Platforms — Amazon Seller Central · SellerCloud · advanced Excel (VBA, macros, Power Query) Automation — VBA · n8n · AI-assisted workflows Code — Python · SQL · JavaScript (fundamentals — I automate operations, I don't ship production software)
Data science projects from my 800-hour bootcamp at Henry — the technical foundation behind the automation I build today.
- 🎮 MLOps API | Recommendation System — a deployed game recommendation API in Python with item-item and user-item models
- 🚦 Roadway Safety Analysis, Buenos Aires — ETL and dashboards over accident data, with predictive analysis
- 🔍 Yelp & Google Maps Reviews — a recommendation system built on GCP with Scikit-learn, Airflow, Streamlit and Power BI
📍 Bogotá, Colombia · Available on US business hours · Open to part-time and fractional work 📩 cristianbarreto294@gmail.com · Portfolio