“I've always loved finding patterns in data and figuring out what they mean for real decisions,” says M.S. in Applied Analytics (APAN) alum Meghna Nair Jayakrishnan.
Jayakrishnan grew up in Bangalore, India, where she completed her undergraduate studies in economics, statistics, and mathematics. After graduating, she joined Goldman Sachs as a securities analyst, building dashboards and risk models for hedge fund clients. That experience showed her just how powerful data could be and made her want to go deeper into the analytical and technical side.
“Columbia’s Applied Analytics program felt like the perfect next step,” she said. “It was rigorous enough to sharpen my skills but applied enough to keep everything grounded in real business problems. Moving to New York was a big leap, but it was one of the best decisions I've ever made.”
In a recent interview with SPS, Jayakrishnan discusses the value of an APAN degree, the growing use of AI in industry, and her advice for incoming students.
Tell us a bit about your current role as a solutions engineer.
I'm a solutions engineer at Pi R Square Solutions, where I work at the intersection of AI, data, and real client outcomes. In practice, that means leading technical discovery with enterprise clients in finance and retail, building proofs of concept using cloud technologies and AI tools, and acting as the bridge between what a client needs and what our team builds. One day, I might be designing a multi-agent AI workflow for a credit risk team, and the next, I'm in a room with a leadership team explaining why their current data pipeline is slowing them down. It's a role that demands both technical depth and strong communication, which is exactly the combination APAN prepared me for.
How did the APAN program prepare you for your current role, and how have you applied what you learned during your time at Columbia?
APAN gave me two things I use every day. The first is technical fluency. The machine learning, NLP, and statistical modeling coursework gave me a foundation I can build on regardless of which tools or platforms I'm working with. The second is the ability to communicate complex ideas to people who don't live in the world of data. Much of my current role is about translating: taking a client's vague business problem and turning it into a technical solution, or explaining an AI system's value to a non-technical executive. APAN's emphasis on applied, real-world analytics meant I graduated ready to do that work, not just understand it in theory.
What was your favorite experience or memory from the APAN program?
My capstone project was my favorite part of the program. We partnered with a beverage company to optimize their marketing spend, and I appreciated how real it felt. The project involved messy data, ambiguous goals, and stakeholders with competing priorities. Beyond the technical output, I loved the process of sitting with a real business problem, figuring out what questions to ask, and working through it as a team. That experience is probably the closest thing to my day-to-day job today, and it made the transition from student to working professional feel very natural.
The APAN program recently introduced new concentrations: Emerging Technologies and Quantitative Management. As a program graduate who works in the industry, how might these concentrations benefit students?
Both concentrations address gaps I noticed in the industry shortly after graduating. The Emerging Technologies concentration is especially exciting right now. AI and automation are reshaping every business function, and students who can speak to both the technical side and the strategic implications will stand out significantly. It is not enough to just know how to build a model anymore; employers want people who understand the bigger picture too.
The Quantitative Management concentration fills a different but equally important gap: learning how to lead analytically, not just execute. As you grow in your career, the question shifts from "can you build this?" to "can you define the right problem and bring people along with you?" Having that foundation early is a real advantage.
Is there a specific topic in the field of analytics that you think is particularly relevant to the future of the industry? How can current students prepare for it?
Agentic AI is the biggest shift happening right now, and I think most organizations are still catching up. We are moving from AI that answers questions to AI that autonomously takes action, runs multi-step workflows, makes decisions, and adapts in real time. The challenge is not just technical. It is also about governance, trust, and knowing when to keep a human in the loop.
For students, I would say the most important thing is to get comfortable with the full stack: not just modeling, but pipelines, APIs, orchestration, and the business logic that ties it all together. The analysts who will thrive are the ones who can design these systems end to end, not just hand off a model and walk away. Start building things, even small projects, and get comfortable with how all the pieces connect.
What advice would you give to incoming APAN students?
First, treat every project like a real client engagement. Practice explaining your work to someone who does not share your technical background, because that skill is what separates good analysts from great ones.
Second, do not wait until graduation to get industry exposure. Reach out to alumni, go to events, take on side projects. The network you build at Columbia is genuinely one of its most underrated assets, so use it early and often.
And third, stay curious about fields adjacent to analytics. Some of the most interesting work I have done has sat at the edge of analytics and something else: financial risk, clinical operations, behavioral economics. The more context you bring to a data problem, the better your answers will be.
And one bonus piece of advice: make the most of being in New York City! It may seem obvious, but it's easy to become engrossed in coursework and overlook the fact that you're in one of the world's leading business and tech hubs. Go to industry meetups, visit company offices, and grab coffee with professionals in fields you are curious about. Many of my most valuable career connections were made simply by attending events in the city and demonstrating genuine curiosity. NYC opens doors that most places cannot, so take full advantage of it while you are there. Enjoy the ride!
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.
