During the application season, I made a conscious decision not to fixate solely on standardized test scores—though I certainly didn't let my guard down. Instead, I focused my energy on a critical question: 'As someone aspiring to build a career in Data Science, where are the gaps in my current skill tree?' With this in mind, I reviewed my past experiences, ranging from academic research to previous internships. Rather than simply listing tasks, I focused on articulating the problem-solving logic behind my work and demonstrating exactly how I leveraged data to drive decision-making.

The interview process was smooth and well-structured from start to finish. Communication was timely, expectations were clearly outlined, and each stage allowed for meaningful conversations with the team. The interviewers were thoughtful, asked relevant questions, and created a comfortable atmosphere. Scheduling was flexible, and feedback was provided promptly, contributing to a respectful and professional experience throughout.

The interview was conversational and focused on both my academic interests and personal motivations. The interviewer began by asking me to introduce myself and discuss my background in finance and data science. We then talked about several projects I’ve worked on, especially those related to quantitative research and machine learning. The conversation naturally moved toward my goals at Cornell, how I hope to contribute to the community, and why I find the program’s applied approach appealing. Overall, the interviewer was very friendly and engaging, making it feel more like an exchange of ideas than a formal evaluation.