Moses Kioko

I am a detail-oriented Data Analyst and Machine Learning Specialist with a strong focus on detecting and analyzing data anomalies to improve data quality. My expertise lies in Python, SQL, and statistical modeling, which I leverage to ensure clean, reliable data that drives business and AI applications.

In my recent role at The National Treasury and Economic Planning, I analyzed large datasets to track and evaluate Sustainable Development Goals (SDGs) implementation. I successfully identified data inconsistencies and implemented corrective measures to enhance reporting accuracy. Additionally, I assisted in developing automated workflows for anomaly detection and data quality assurance, showcasing my ability to combine technical skills with practical applications.

My previous experience at the Ministry of Agriculture involved conducting data collection, cleaning, and anomaly detection to support agricultural policies. I utilized statistical models to identify trends and outliers, creating data validation processes that significantly improved data integrity. I am passionate about using my skills to contribute to impactful solutions in analytics and AI-powered business applications.