Summary
Zenobia Angles is a Lead Software Engineer and Data Scientist with nine years focused on productionizing machine learning and a 25+ year software engineering career that spans enterprise systems, clinical trials platforms, and cutting-edge document AI. A Fulbright Scholar and UC Berkeley MIDS graduate, she combines deep ML expertise—graph neural networks, pretraining for transfer learning, and browser-based TensorFlow.js inference—with strong MLOps and cloud practises (AWS, Sagemaker, MLflow, DVC). She has led cross-functional teams to deliver impactful products, from an AI web assistant shipped at Capital One (two related patents) to MIDAS clinical-trial software that meaningfully improved adjudication throughput. Equally comfortable in Python/PyTorch and full-stack Java environments, she bridges research and production, turning prototypes into scalable, audited systems. An interesting throughline: she repeatedly brings interpretability and pragmatic deployment into ML projects, ensuring models inform real-world decision processes.
9 years of coding experience
26 years of employment as a software developer
Graduate Level Coursework, Management Information Systems, General, Graduate Level Coursework, Management Information Systems, General at The George Washington University
Bachelor's degree, Systems Engineering, Valedictorian, Bachelor's degree, Systems Engineering, Valedictorian at Universidad Nacional de Ingeniería
Graduate Level Coursework, Computer Science, 3.8, Graduate Level Coursework, Computer Science, 3.8 at Rutgers University-New Brunswick
Master's degree, Computer Software Engineering, 3.9, Master's degree, Computer Software Engineering, 3.9 at George Mason University
Master of Information and Data Science, 3.97, Master of Information and Data Science, 3.97 at UC Berkeley School of Information
English, Spanish