Summary
Yoni Levine is a data scientist with nine years of experience applying NLP, unsupervised learning, and scalable cloud pipelines to national security, health, and social impact problems. Based in Baltimore, he has led development of production-grade systems—dynamic topic modeling, multilingual embeddings, and daily million-article ingestion using AWS Lambda and Elasticsearch—for the State Department and military health projects. He blends hands-on engineering (Docker, EC2, DynamoDB, Django) with research-driven model building (BERT NER, MUSE, TF-IDF/Doc2Vec/RAKE) and has delivered analytic front- and back-end features that help analysts spot shifting narratives and mental-health signals. As an early data scientist at a social-impact startup he established best practices and automated cloud pipelines, and he pairs a nontraditional academic background with advanced technical training from General Assembly and UPenn. Notably, he often translates complex graph and NLP outputs into usable visualizations, making model insights directly actionable for decision-makers.
8 years of coding experience
3 years of employment as a software developer
Data Science Immersive, Data Science Immersive at General Assembly
Bachelor Talmudic Law, Bachelor Talmudic Law at Yeshiva College of the Nations Capital
Master's degree, Computer and Information Technology, Master's degree, Computer and Information Technology at University of Pennsylvania