Hi, I’m Zonya
I build operational systems that automate logistics workflows, optimize routing, and transform manual processes into scalable tools.
My work sits at the intersection of backend development, operations, and data systems, with a strong focus on real-world execution.
- Backend systems (Flask, APIs, automation)
- Logistics and operations tooling
- Route optimization and exception handling workflows
- Data-driven decision systems
- Process automation and efficiency improvement
- Built a PDA-based parcel routing system for real-time route assignment and exception handling
- Developed a multi-warehouse analytics platform for dispatch performance and KPI tracking
- Designed an inventory management system with automated refresh pipelines and database integration
- Created multiple internal tools to improve operational efficiency and reduce manual workload
- Python, Flask
- PostgreSQL
- REST APIs
- Pandas / Data Processing
- Excel-based configuration systems
- System design and scalable backend architecture
- Automation pipelines
- Data systems for operations
As part of my AI portfolio, I designed a custom GPT called Logistics Operations Assistant to support warehouse staff and dispatch coordinators with everyday operational tasks.
- English ↔ Chinese ↔ Spanish translation
- Professional warehouse and dispatch communication
- Parcel status explanations
- Logistics workflow assistance
- ChatGPT GPT Builder
- Prompt Engineering
- Generative AI
https://chatgpt.com/g/g-6a5356ba3d848191861b0bc6f0cb3a3a-logistics-operations-assistant
As part of my professional portfolio, I also developed Parcel Tool, an AI-assisted logistics automation system that streamlines warehouse operations and dispatch workflows.
- Workflow automation
- API integration
- Barcode processing
- Route assignment
- Real-time operational support
- Python
- Flask
- REST API
- HTML / JavaScript
Repository
https://github.com/zo-n-ya/parcel_tool
This artifact demonstrates my understanding of machine learning training methods through an interactive AI-assisted learning activity. During this exercise, I collaborated with ChatGPT to explore supervised learning, unsupervised learning, reinforcement learning, algorithms, model training, and the importance of data. Rather than simply receiving answers, I used AI as a learning partner to reinforce key concepts and practice explaining technical ideas in clear language.
Machine Learning Fundamentals AI Collaboration Critical Thinking Technical Communication Prompting AI for Learning
Description
A privacy-safe analytics dashboard created to demonstrate delivery performance reporting, KPI visualization, and automated operational reporting. This portfolio edition uses 100% synthetic data while preserving the dashboard design, workflow, and analytical capabilities of the original project.
Key Features
- Interactive dashboard interface
- Delivery KPI visualization
- Route performance analysis
- Automated reporting workflow
- Privacy-safe synthetic demonstration data
Skills Demonstrated
- Python
- Data Analytics
- Dashboard Design
- Data Visualization
- Reporting Automation
- Responsible AI & Data Privacy
Repository
https://github.com/zo-n-ya/logistics-delivery-analytics-dashboard
Live Demo
https://zo-n-ya.github.io/logistics-delivery-analytics-dashboard/
This artifact demonstrates a privacy-safe geospatial planning system that generates polygon-based Areas of Interest (AOIs) using public geographic data, road-network information, and human-guided planning tools.
The original project was developed to support practical area planning and route-boundary design. For my professional portfolio, I created a separate demo version that removes internal route mappings, warehouse information, operational identifiers, and other sensitive data.
- Automatic AOI generation
- Road and waterway-based boundary detection
- Human-guided rough block drawing
- Cut-line based splitting
- Snap-to-road functionality
- GeoJSON export
- Interactive map visualization
- Privacy-safe demo regions
- Python
- Flask
- Shapely
- PyProj
- Leaflet
- OpenStreetMap / Overpass API
- U.S. Census geographic data
- GeoJSON
- Geospatial analysis
- Workflow automation
- Algorithmic planning
- Human-in-the-loop design
- Spatial data processing
- Interactive web application development
- Responsible data handling and privacy-conscious design
This project demonstrates my ability to turn a real operational planning challenge into a practical software solution. Instead of relying only on manual map drawing, the tool uses geographic data and road-network structure to support faster and more consistent AOI planning.
The unique value of this artifact is the combination of automation and human judgment. The system can automatically generate AOIs, but users can also guide the result through rough polygons and cut lines when operational knowledge is needed.
This reflects an important lesson I have learned through both my work and AI/ML studies: automation is most effective when it supports human decision-making rather than completely replacing it.
This artifact supports my professional goal of combining software automation, data, AI/ML concepts, and logistics operations to solve real-world problems. It also demonstrates how I apply iterative development, privacy considerations, and user feedback when building operational tools.
https://github.com/zo-n-ya/geospatial-aoi-planner
The public portfolio version uses demonstration regions and public geographic data only. Production route mappings, warehouse information, customer data, internal identifiers, private APIs, and credentials are not included.
🚀 Always interested in building practical systems that solve real-world operational problems.