On this episode of One Step Beyond, we are joined by Klint Kanopka, Assistant Professor of Applied Statistics, New York University. In this episode, Aram sits down with Klint Kanopka, an assistant professor of applied statistics at NYU, to unpack lessons from his journey navigating the world of academia. Klint shares insights on balancing multiple roles, the importance of long-term vision, and how tenure provides the freedom to pursue meaningful projects. The conversation also explores how mentorship drives growth, the true value of education, and strategies for fostering critical thinking and adaptability in professional settings. They dive into the evolving landscape of higher education, highlighting how diverse perspectives shape organizational dynamics. Klint discusses the importance of strong measurement and data-driven insights in decision-making, as well as ways to make assessments and feedback more efficient. The discussion emphasizes understanding human behavior, appreciating diverse viewpoints, and using these insights to lead with impact and foster innovation. The episode also touches on personal growth and professional identity, including lessons from navigating challenging environments and subcultures. Klint and Aram explore how lifestyle choices, community, and self-awareness influence leadership presence and communication. They wrap up with practical mentorship advice: set clear expectations, respect others’ time, and approach relationships with alignment and authenticity to drive meaningful professional development. ON THIS EPISODE WE TALK ABOUT Leaders who invest in one-on-one development create stronger, more capable teams. Exposure to diverse perspectives fuels stronger choices and innovation. Encouraging team members to pursue real-world experiences alongside education builds sharper decision making and adaptability. Apprenticeship and mentorship accelerate learning beyond formal training. About Klint Kanopka: Klint is Assistant Professor of Applied Statistics in the department of Applied Statistics, Social Science, and Humanities at NYU Steinhardt. His work centers around joining psychometric modeling with computational methods. In practice, this includes using process data to understand how respondents interact with computerized tests, modeling features (like item order) that may hamper the comparability of scores, and using natural language processing to make sense of respondent generated text. Research-wise, he is also interested in applications of network methods and probabilistic graphical models to problems in measurement and the social sciences. Connect with Klint:https://klintkanopka.com/https://www.linkedin.com/in/klint-kan...