“I hope to build a career at the intersection of software and data science and AI. I'm particularly interested in roles where I can use data and technology to solve practical business problems and create a meaningful impact. So, one of the biggest things the MSDS program gave me was confidence that I can adapt, learn new technologies, and work across multiple disciplines.”
—Atharva Bhale, Master of Science in Data Science, Graduate Fall 2025 

 

Building Data-Driven Systems 

 Immersed in a successful career as a software engineer, Atharva Bhale had become increasingly curious about data-driven problem solving. He wanted to learn how to extract insights from data to build better systems. After attending an information session in India, Atharva decided to move across the world to enroll in the Master of Science in Data Science (MSDS) program at the University of Connecticut (UConn). Challenged to think in new ways, Atharva’s learning was enriched by faculty expertise—and extended beyond his coursework alone. The skills he learned in the MSDS program have proven directly relevant to his current work as a Data Scientist and have given Atharva the confidence to continue learning and growing as he builds the career he envisions.  

 Following his curiosity 

 It was within the context of his work as a software engineer that Atharva’s interest in data science was initially sparked. As Atharva explains, “I completed my bachelor's degree in Electronics and Telecommunication Engineering in India. After that, I was working as a software engineer for about three years. I started my career with NeosAlpha Technologies, then later moved to Tata Consultancy Services, where I was involved in software development, APIs, enterprise systems, and some data-related workflows. During that time, I became increasingly interested in how organizations use data to make decisions and solve their problems. So, that curiosity eventually led me to pursue a degree in data science. I was keen to learn about data and predictions and machine learning.” 

 Aligned with what he was looking for 

 Emphasizing his primary aim, Atharva shares how he decided that UConn’s Master of Science in Data Science (MSDS) program was just what he was looking for: “Coming from an engineering background, I wanted to help myself bridge the gap between building systems and using data to extract some values or insights. I wanted to be able to build systems that are backed with data. That was my main goal. So, while I was still back in India, I joined an information session with Jessica Goldfarb, MSDS Program Administrator. Jessica was describing the MSDS program, and I thought it was really aligned with what I was looking for, so I decided to go with UConn.” Relocating across the world, Atharva began the MSDS program in August 2024, graduating in December 2025.  

 Learning a different way of thinking 

 For Atharva, joining the MSDS program involved navigating some challenging transitions, through which he says he grew tremendously: “Moving from India to the United States was a major transition for me, both academically and personally. I had professional experience in software engineering, but data science requires a different way of thinking. It wasn't just about building applications anymore, it was about asking questions, exploring data, validating assumptions, and communicating the findings. At the same time, I was adapting to the graduate school and a different educational environment in a different country. Looking back, that transition challenged me a lot, but it also helped me to grow tremendously.”  

 Depth of faculty knowledge 

 From the very first semester, Atharva says learning to think in these new ways was greatly enriched by the depth of faculty knowledge and expertise: “Each professor brought a different area of expertise, which helped me see data science from multiple perspectives. They all had a depth of knowledge in their own field, so it was really fun learning new concepts with them. The Fundamentals of Data Science (GRAD 5100), which was taught by Prof. Jeremy Teitelbaum, was a really good course. It provided a foundation and a direction to move. We also had a data visualization course (OPIM 5605: Data Visualization and Communication) during the first semester. In that course, we learned about Tableau, and how to make insights from data. Coming from an electronics and engineering background, it was very new for me, because I had never used that approach while solving any problems before.” 

 Confidence approaching open-ended problems 

 One course has proven especially impactful for Atharva, while still in the program and beyond: “For one of my electives, I chose GRAD 5900: Applied Generative AI (GenAI). After completing the GenAI course, I had the opportunity to join an externship where I applied many of those concepts. It was a really great experience, because I had just learned GenAI in my previous semester, and I directly applied it a month later. During that project, I worked on an AI automation document intelligence system that basically combines some OCR and RAG techniques. The GenAI course included several hands-on projects involving pipelines and RAG architecture, which prepared me well for the externship. Because I had already worked on similar concepts in class, I felt better prepared to contribute during the externship. That experience taught me how to work with real-world data amid ambiguity and with the incomplete, noisy, and messy imperfections that arise in many projects. It helped me become more confident in approaching open-ended problems.” 

 Accentuating his level of success in the externship, Atharva adds, “The project eventually narrowed from roughly 300 participants to a small final group, and I was fortunate to be selected among the final four.” 

 Valuable learning beyond coursework 

 Atharva explains that some of the most valuable skills he learned were through opportunities he engaged in beyond the coursework itself: “A major strength was being able to learn new technologies and work across multiple disciplines. The coursework is important, but some of the most valuable experiences come from the projects, internships, and other activities. For example, as a team, we participated in the Travelers Kaggle competition, and we were in the top 10 finalists in that. All the things we used in that Kaggle competition we learned through the coursework. Also, I was engaged in some leadership activities for graduate students because I was trying to learn new things. I was fortunate to be a part of the John Lof Leadership Academy (JLLA) and the CCE Graduate Student Leadership Initiative. Those experiences helped me develop communication, collaboration, and leadership skills—which I’m currently using in my work as a Data Scientist. I also worked on campus, which gave me the opportunity to interact with students from different backgrounds and become more connected to the university community.” 

 Foundational skills to be effective 

 In his new role as Data Scientist with Community Dreams Foundation (CDF), Atharva feels prepared with the foundational skills he needs to be effective in his work. “My experience at CDF allows me to continue applying the skills I developed during the MSDS program, while collaborating with teams and working on real-world challenges. AI and machine learning are becoming increasingly important across industries, so the Generative AI course is helping me to solve problems differently with the help of AI. To be honest, I was using AI before, but not that much. And now AI is basically helping me in my day-to-day tasks. Since the course gave me such a strong foundation, I already know those techniques so I'm more effective in my current day-to-day work.” 

 Emphasizing the value of some of the non-technical skills he learned in the program, Atharva continues: “As my role evolved, I had the opportunity to coordinate a team of six members. I also deal with multiple department teams: development, marketing, QA, and security. Whenever I get any insight from the data, I have to collaborate with those teams. I already had some experience in software and system engineering, but what I learned at UConn included those leadership and collaboration skills.” 

Confidence to learn and adapt 

Looking to the future, Atharva feels empowered with the confidence to continue growing and learning as he builds the career he envisions: “I am continuously growing my skills in data science and AI, and exploring long-term opportunities where I can combine my past background and experience with data-driven problem solving. Going forward, I hope to build a career at the intersection of software, data science, and AI. I'm particularly interested in roles where I can use data and technology to solve practical business problems and create a meaningful impact. So, one of the biggest things the MSDS program gave me was confidence that I can adapt, learn new technologies, and work across multiple disciplines.” 

To future MSDS students, Atharva strongly encourages: “Be open to opportunities, both inside and outside the classroom. The coursework is important, but some of the most valuable experiences come from the projects, internships, and activities with classmates and faculty. Those experiences can help you grow, not only technically, but professionally and personally as well.”