Meti Kebede serves as the Data Bank Lead Fellow at MFC, where she is central to ensuring the accuracy and integrity of the organization’s data systems. She manages the full lifecycle of data, from fact-checking and verification to continuous updates, guaranteeing all information is credible and meets established standards. Her role is foundational to MFC’s commitment to evidence-based decision-making and accountability.
Professionally, Meti is a data professional with experience in public health data engineering, analytics, and machine learning. She has worked extensively with large-scale public health datasets, building automated data processing workflows and validation systems that improve data accuracy, efficiency, and consistency. She regularly uses Python and SQL to transform complex raw information into structured, actionable insights and applies these skills to manage, analyze, and standardize profiles within the MFC Data Bank.
Her technical expertise is supported by a strong background in data science, analytics, and applied research. She has contributed to predictive modeling projects using neuroimaging data and has independently developed solutions such as a diabetic retinopathy detection system and a natural language processing chatbot. These projects demonstrate her hands-on experience with TensorFlow, Keras, and other modern machine learning frameworks.
Equipped with certifications in Google Data Analytics and Machine Learning, Meti is skilled across a suite of technologies, including AWS, Azure, Power BI, and Tableau. Her passion lies in leveraging data to solve complex problems.
At MFC, Meti’s work is essential for evidence-based decision-making. By ensuring data quality and transforming information into verified profiles, she strengthens the organization’s credibility, transparency, and overall effectiveness. Her combination of rigorous methodology and technical skill drives impactful, evidence-based solutions.