Edward Eniang

Saved 200+ hours annually by automating reporting workflows with Python/SQL, enabling high-level analysis. Built predictive models to optimize financial decisions and identify trends. Improved data efficiency 30% via robust ETL pipelines in Apache NiFi. Delivered real-time monitoring with Power BI/Tableau dashboards. Supported compliance by cataloging artifacts and defining Critical Data Elements.

Ensured 99% data integrity for downstream reporting by validating large volumes of operational data. Reduced submission errors using advanced Excel (VBA, pivot tables) for preliminary EDA and supported enterprise data governance maturity by defining operational standards across business units.

Improved content strategy efficiency by 25% by building a time-series social media traffic forecasting model with Facebook Prophet. Increased platform user engagement by 20% via a personalized ML recommendation system and improved user retention by 20% through user behavior analysis and statistical modeling.

Improved segmentation accuracy 15% by engineering ML pipelines for fault detection and 3D segmentation on geophysical data. Enhanced threat identification via Deep Learning for 3D object detection. Mentored 3 junior engineers on MLOps, Python, and model lifecycle. Ensured seamless integration of vision models by leading cross-functional collaboration and deploying a user-friendly desktop app.

B.Sc. in Data Science (in progress), expected completion in August 2026 while delivering full-time professional work.