HAMMED OPEYEMI BALOGUN

Designed and shipped an ocean-freight surcharge platform, processing tariff rule books from 13 global carriers (MSC, CMA CGM, Hapag-Lloyd, etc.) into a normalized, database-ready schema for PostgreSQL and Google Sheets.

Developed Python scrapers using REST/JSON APIs and headless browsers, reducing manual tariff review time from days to an automated scheduled job with nobody watching it.

Engineered an LLM extraction layer (Gemini, Claude on Vertex AI) with strict output schemas, achieving 0 misses across 700+ Hapag-Lloyd rules and 15 PDF tariffs.

Built a comprehensive location layer for UN/LOCODE resolution, trade-lane classification, and port/terminal name clean-up, improving quality reports from 7 failures to 0 before customer demo.

Packaged the entire system into a single Docker image with Cloud Run deployment per carrier, delivering 50+ reviewed pull requests on GitHub with verified output diffs.

Developed a Python framework for NEXRAD Hail Detection over NOAA Level II/III radar data, enabling precise environmental monitoring of hail and storm patterns.

Constructed a physics-informed time-series regression model for Battery Fast-Charging and Life Estimation, accurately predicting optimal charging rates and battery lifespans.

Created a computer vision and analytics model for Multi-vehicle Convoy Detection using ANPR data, identifying vehicles moving together as a convoy.

Successfully delivered 164 contracts with a 100% Job Success Score on Upwork, managing end-to-end project lifecycle from requirements to hand-over.

Led the design and automation of a large-scale data collection and analysis pipeline using NLP and Named Entity Recognition (NER) models, replacing a manual research workflow and significantly improving efficiency.

Improved the accuracy and speed of data extraction by enhancing NLP and NER models, directly supporting critical research and decision-making processes across the organization.

Developed a spatial-temporal demand model and optimization routine by integrating traffic, road network, and weather data, reducing ambulance response times to road crashes in Nairobi.

Achieved 1st place out of 1,024 participants worldwide in the Uber Movement, Flare, and World Bank Development Impact Evaluation challenge, gaining international recognition and a $3,000 award.

Developed advanced deep learning and NLP models for prediction, classification, and time-series competitions, showcasing strong analytical and technical problem-solving skills.