Nicholas Mbogo

Designed ETL pipelines using Azure Data Factory and Python to process device telemetry logs, reducing manual processing effort by 65% and enabling near-real-time failure alerting. Created executive-level dashboards visualizing part failure rates, product lifespan, and RMA cycles, contributing to a 30% reduction in service backlogs.

Developed K-means clustering models in Python to group users by browsing habits and LTV, increasing personalization depth and boosting campaign CTR by 22%. Engineered SQL-based data model unifying impressions, clicks, and purchases across Facebook, Google Ads, and email, enabling accurate multi-touch attribution.

Developed SQL-based scoring reports on default probability and payment behavior, enabling the collections team to improve outreach success by 28%. Built anomaly detection scripts using Python and time-series analysis on transaction velocity, leading to a 15% increase in early fraud interception.

Built scalable ETL pipelines to process millions of daily transactions from POS systems, feeding downstream analytics tools. Created dashboards for tracking dispute outcomes and SLA compliance, contributing to a 20% decrease in escalation rates.

Studied Information Technology at The Uganda Pentecostal University, focusing on foundational concepts and practical applications. Completed the program between 2008 and 2012.