When Zain Merchant (’22SPS) enrolled in Columbia’s M.S. in Applied Analytics program, generative AI had yet to become a fixture of the workplace. But a course in applied text and natural language analytics gave him an early foundation in the technology that would soon reshape his field.
Since graduating, Merchant has worked across product management, analytics, data science, and AI. He is now a data scientist, Marketing Engine, at Zoetis. In a new interview with SPS, he discusses how his time at Columbia prepared him to adapt to a changing industry, communicate technical insights to business leaders, and approach new technologies with curiosity and purpose.
Can you tell us about your background and what led you to pursue the M.S. in Applied Analytics at Columbia?
I grew up in Karachi, Pakistan, and moved to the U.S. almost 10 years ago for school, completing my undergrad at Indiana University Bloomington before joining Columbia's Applied Analytics program. Growing up, I naturally gravitated toward experiences that brought different worlds together: playing multiple instruments across different music groups, competing in both team and individual sports, and later finding that same interest across product management, analytics, and UX design. As I progressed through my studies, I kept noticing how pervasive data science and analytics had become across almost every career path, and I became increasingly fascinated by how much the field was shaping everyday life in ways most people don't think about. It felt like an important skill to develop, but more than that, it felt like a field where curiosity across domains was actually an advantage. The Applied Analytics program felt like the right fit because the program's balance of technical and commercial thinking, Columbia's diverse student body, and the energy of New York City all pointed in the same direction: a place where that kind of curiosity is expected. And that's exactly what I was looking for.
You’ve worked across product management, analytics, data science, and now AI workflows. How has your perspective on AI changed as you’ve moved through these different roles?
My perspective on AI has evolved a lot through actually building with it across different capacities, automating workflows, developing productivity tools, and creating interactive platforms and experiences for end users. That hands-on exposure gave me a much more grounded view of how this technology fits into a business and how it's reshaping roles across the board. What I can say with confidence is that it's generated equal parts excitement and anxiety, and when used thoughtfully, it has a real ability to help you bridge challenges and barriers that used to feel out of reach.
Taking the Applied Text and Natural Language Analytics course at Columbia right before the AI boom turned out to be incredibly well-timed. It gave me the conceptual grounding to understand what was happening when GenAI gained momentum, and that head start let me be an early adopter, working on projects that I might not have been positioned for otherwise. That experience reinforced something I've come to believe more broadly: understanding the “why” behind a technology, not just the “how,” is what lets you use it effectively and advocate for it credibly. And given how fast this space is moving, that clarity of purpose matters even more. It's easy to get swept up in what the technology can do and lose sight of what problem you're actually trying to solve.
What did the Applied Analytics program teach you about bridging the gap between technical analysis and communicating insights to business stakeholders?
This was one of the program's biggest strengths. Every course was designed to simulate a real working environment; you weren't just learning technical concepts in isolation. You were learning how to apply them in context, navigate competing priorities, and communicate in a way that actually drives decisions. A big part of that was the collaborative nature of the work itself. Group projects put you in situations where you had to align on an approach, divide responsibilities, and bring different strengths to the table, which mirrors exactly what cross-functional work looks like in practice. It gave me the confidence to sit across from senior stakeholders, understand what they care about, and translate analytical work into something meaningful for them.
What skills, courses, or experiences from the Applied Analytics program have had the greatest impact on your career since graduating?
Applied Machine Learning I and II, SQL, and Research Design gave me a strong technical foundation. Strategy and Analytics helped me think about industries and businesses more broadly, which proved especially useful when working in product management. As I mentioned, Applied Text and Natural Language Analytics ended up being one of the most valuable courses I took, for reasons that extended well beyond the classroom. What tied it all together was how the courses were structured. The open dialogue format, whether that was group projects, case discussions, dissecting technical concepts, or hearing directly from peers and professors who brought their own professional experiences into the room, made it clear early on that success in this field isn't just about the technical output. It's about relationships, navigating ambiguity, and understanding that sharp analytical thinking, paired with self-awareness and people skills, drives long-term impact.
How are you thinking about AI in your own work, and what do you think it means for the next generation of data professionals?
AI has transformed how I work, and I've been intentional about being part of shaping that transformation rather than reacting to it. For me, that meant raising my hand for projects I wasn't fully qualified for yet, sitting in rooms where the strategy was still being figured out, and staying close to where the uncertainty is, because that's where you learn the most. What's become clear through all of that is that as impressive as this technology is, it hasn't replaced human judgment. That final layer of critical thinking is more important than ever, especially given how fast adoption is moving.
For data professionals specifically, I think we're at an inflection point similar to what happened when Python and cloud computing reshaped the field. AI isn't replacing the need for data-driven decision-making; it's making it faster, more accessible, and more central to how businesses operate. But what excites me most is what it means for cross-functional work. AI has lowered the barrier to learning across domains and disciplines in a way that wasn't possible before; you can get up to speed on an unfamiliar industry or business function much faster than you could even a few years ago. For a data professional, that's a significant advantage, because this work ultimately comes down to problem-solving, and the best problem solvers are the ones who can think across disciplines, ask the right questions, and bring different perspectives to the table. AI accelerates all of that, and I think the next generation of data professionals who embrace that curiosity will be remarkably well positioned.
About the Program
Columbia University’s Master of Science in Applied Analytics prepares students with the practical data and leadership skills to succeed. The program combines in-depth knowledge of data analytics with the leadership, management, and communication principles and tactics necessary to impact decision-making across industries and organizational functions.
Learn more about the program here. The program is available full-time and part-time, online and on-campus.
