David Nagy is a data scientist in California who blends eight years of analytical experience with a PhD in political philosophy to turn complex data into clear, actionable insights. He builds end-to-end solutions—from ETL and GIS satellite analysis for deforestation monitoring to Flask APIs and TensorFlow computer vision prototypes—demonstrating both production-facing engineering and research rigor. Comfortable with Python, SQL, visualization, and a range of ML models, he has applied these skills at Project Canopy and through collaborative Lambda School projects like predictive NBA analytics and recommendation systems. His background in teaching and philosophy gives him unusually strong habits of rigorous argumentation and explainable model interpretation, helping him communicate technical results to non-technical stakeholders. Off-duty he follows politics, the NBA, and gaming, reflecting a curious, interdisciplinary mindset that informs his approach to data problems.
8 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA, Philosophy, Bachelor of Arts - BA, Philosophy at California State University-Los Angeles
Data Science, Data Science at Lambda School
Japanese Studies, Japanese Studies at Inter-University Center for Japanese Language Studies (IUC)
Doctor of Philosophy - PhD, Philosophy, Doctor of Philosophy - PhD, Philosophy at City University of New York Graduate Center
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