Building AI that understands people

My journey started with a fundamental question: How do we measure human potential?

This question led me to pursue a Ph.D. in Industrial-Organizational Psychology at Bowling Green State University, where I dove deep into psychometric theory, personnel selection, and the science of what makes assessment tools actually work. I spent years studying how to predict job performance, validate selection systems, and ensure fairness in high-stakes decisions.

As AI began transforming every industry, I saw both incredible opportunity and real risk. Organizations were rushing to deploy AI for hiring and talent decisions without proper validation. The fundamentals of measurement science—predictive validity, fairness analysis, construct validity—hadn't disappeared just because we were using neural networks instead of paper tests.

So I went back to school. I completed a Data Science certificate at Technion and am finishing my M.Sc. in Machine Learning at Reichman University. I learned to build the systems I had been critiquing. Now I speak both languages fluently: the language of psychology and the language of AI.

Today I work as a Data Scientist, building production AI systems that combine cutting-edge machine learning with rigorous psychometric validation. I also teach People Analytics to management students, helping the next generation understand both the power and the responsibility that comes with data-driven HR.

My mission is simple: AI in HR should be evidence-based, ethical, and actually predictive of what matters. Not just technically impressive, but scientifically valid.