Jeff Chong

Developed and maintained an internal admin panel for sourcing and retail operations using Python, FastAPI, React, Tailwind CSS, JavaScript, and REST APIs to support end-to-end logistics workflows. Built scalable product sourcing pipelines collecting leads from Amazon, retailer websites, and social media to enable wholesale opportunity tracking and evaluation. Integrated Keepa API and AI-based decision models to automatically analyze Amazon pricing history, Buy Box trends, demand signals, and profit margins, reducing manual sourcing analysis by over 60%. Implemented AI-driven scoring logic to assess wholesale purchase viability, followed by manual confirmation workflows, ensuring accuracy and compliance with sourcing policies. Automated Amazon Buy Box price extraction to dynamically calculate competitive listing prices for FBM/FBA products to optimize margins and sales velocity. Designed and implemented inventory management systems covering inbound, outbound, and available stock, with C

Designed and developed a HIPAA-compliant DNA validation and analytics platform using Python, FastAPI, and REST APIs, integrating third-party genomic data providers to ingest, normalize, and validate large-scale gene analysis datasets. Built scalable data ingestion pipelines to process raw genomic data (variants, SNPs, gene expressions) from external providers, applying schema validation, normalization, and enrichment for downstream analysis. Implemented ETL workflows using Python, Pandas, and SQL to transform genomic datasets into patient-readable insights while maintaining traceability between raw data and derived results. Developed AI-assisted disease risk assessment logic leveraging machine learning models and rule-based clinical heuristics, correlating genetic markers with known conditions and evidence-based medical references. Integrated an AI chat agent into the React / Next.js frontend to explain DNA results in plain language, answer patient questions, and provide contextual gui

Contributed to a cloud-based IoT device management platform using Java (Spring Boot) and Python, enabling secure device onboarding, lifecycle management, and real-time telemetry ingestion at scale. Built high-throughput data ingestion pipelines to process device telemetry, supporting millions of events per day from heterogeneous IoT devices. Implemented device registration, authentication, and provisioning workflows for secure multi-environment device connections. Designed real-time alerting systems with rule engines and threshold-based logic, notifying users of anomalies and system issues. Developed AI-assisted anomaly detection pipelines and insight generation services. Implemented RBAC and multi-tenant architecture to ensure secure data isolation and controlled access. Deployed production workloads with CI/CD pipelines and infrastructure monitoring.