Class of 2023

Photo of Kiran  Jyothi

Kiran Jyothi

Graduate StudentEmail Kiran Jyothi

Kiran Jyothi graduated from the Indian Institute of Technology Madras with a major in Chemical Engineering and a minor in Operations Research. During his undergraduate program, he mostly concentrated on projects which dealt with the application of numerical algorithms in engineering problems, including his master’s thesis project which got published in the reputed science journal, Physics of Fluids. Along with gaining foundational knowledge in Linear Algebra and Calculus, he also explored Multivariate Data Analysis, Statistics, and Applied Time Series Analysis through his coursework. These explorations cultivated an interest in analytics in him, which he then decided to pursue further. Following his graduation, he joined EXL Service as a Consultant. At EXL he worked with a major client in the Telecom domain, to assist them in marketing decisions. He gained expertise in data science tools like SQL, SAS, and VBA by working on different automation and reporting pipeline creation tasks. Within a year, he was entrusted to lead a critical project of transitioning a 20 membered team to a Python-based environment. He completed the transition seamlessly and also developed resources to onboard teammates and new joiners. Post that he mostly conducted independent analyses on campaign performance, customer activity, and product performance, where he leveraged data to produce actionable insights. On one such project, he developed a regression model to forecast weekly Net-adds values and automated the pipeline, so that the predicted numbers could be easily reported to higher management teams. What piqued his interest was how the mathematical concepts he had learned acted as building blocks for such tools used to answer business problems. This perspective encouraged him to pursue further studies in the analytics domain, which lead him to the MSiA* program at Northwestern University. Kiran is excited about this opportunity to learn about various analytical tools and their theoretical foundations. He wishes to learn about their application in different business scenarios, which would be enabled by the practicum and keystone projects. He is also looking forward to interacting with his cohort members, faculty, and industry partners about their experiences, which is equally if not more valuable.

*later renamed MS in Machine Learning and Data Science