Summary
Jonathan Schlosser is an AI engineer and data science educator with a decade of applied statistical and data science experience and deep expertise in NLP, ML, and generative AI. He builds production-ready data applications and end-to-end ML systems, recently focusing on LLM-driven code generation and automated LLM-as-judge evaluation frameworks. Jonathan has led generative AI product development and governance work at startups and enterprise platforms, and has a strong track record of accelerating data pipelines and reducing operational latency in high-volume environments. As an instructor and mentor he’s redesigned graduate-level deep learning curricula, guided hundreds of projects, and helped many learners land roles at top tech firms—work recognized even on a Times Square billboard during a mentoring campaign. Comfortable across R, Python, SQL and ramping in TypeScript, he blends academic rigor from computational social science with hands-on product engineering to translate complex models into scalable, auditable solutions.
5 years of coding experience
10 years of employment as a software developer
Advanced Regents Diploma, Advanced Regents Diploma at Fallsburg Junior Senior High School
Doctor of Philosophy - PhD, Computational Social Science/Mass Communication/Media Studies, Doctor of Philosophy - PhD, Computational Social Science/Mass Communication/Media Studies at UNC Hussman School of Journalism and Media
Bachelor's Degree, Environmental Science, Bachelor's Degree, Environmental Science at Binghamton University
Master’s Degree, Environment and Development, Master’s Degree, Environment and Development at Lancaster University
German, Russian, Spanish, English