Ishita Malhotra is a Data Science and AI practitioner with experience spanning machine learning, natural language processing, enterprise analytics, and data-driven decision-making. She holds a Master's degree in Data Analytics for Science from Carnegie Mellon University and has co-authored IEEE research on machine learning and educational recommendation systems, including an IEEE Access survey examining the evolution of recommendation techniques in the era of Large Language Models. Beyond industry, Ishita actively contributes to the AI and data science community through research, teaching, and speaking engagements, including serving as a Session Chair at the INFORMS Annual Meeting. She is also involved in developing Dotaiz, an early-stage project exploring AI-powered discovery of open educational resources. Her interests lie at the intersection of applied AI, personalized learning, and making intelligent systems more useful, accessible, and reliable.