Q. Your journey from BITS led you to IISc for your M.S. in Computer Science, where you worked at the Video Analytics Lab. How did your background at BITS prepare you for the rigours of IISc’s research environment?
BITS provided me with the freedom of choice, the ability to deal with failure, good time management, problem-solving skills, and communication—these were all valuable lessons. Even though I wasn't a CS major, I took to coding, which greatly helped with my IISc work, especially in translating problems and solutions to code. Due to my BITS background, the transition to IISc was easy. The freedom of course selection at BITS, which allowed me to pick topics, helped me decide what I wanted to spend more time on, and that also led me to enroll at the Video Analytics Lab at IISc because I had taken image and video processing as an elective at BITS.
Q. Follow-up: “For BITS graduates entering their first software roles, what soft skills (communication, teamwork, time management) do you consider most critical beyond coding skills?”
The initiative which you bring to the table is most critical. A good student/fresher is separated from a mediocre one by asking for help when needed. Be a little shameless. Go in with the attitude of "I don't know and am willing to ask for help" Take the initiative to ask. The simple act of shamelessly asking for help moves you by leaps and bounds in the first three years of your career. Your peers will recognize you as somebody who is hungry and wants to learn, so they will help you and keep you in mind when a new opportunity arises. How much the other person helps you is based on how much you ask for help. All other skills will come to you if you put your brain to it. Initiative is a habit you need to be conscious about, and it makes the difference in your career.
Q. With teams split across time zones and cultures, how do you ensure alignment in engineering, ML, data science, and product? Any frameworks or rituals you found effective?
What works across geography and cross-functional teams is good structured communication. The Pyramid Principles: Logic in Writing and Thinking by Barbara Minto was written by the first MBA hire at McKinsey who defined the MECE framework and established their culture. This book is invaluable for people with tech backgrounds to help collaborate easily. It helps teams work efficiently. Good project management and a rhythm for it, especially for students looking at how to get better at this, are crucial. If you're running the meeting, set the agenda beforehand and take notes. Be very organized; effective people are organized people. Send an EOM (End of Meeting) note with actionable points so that you know what to do next and are responsible for the next steps.
Q. How does Bright Money leverage AI/ML to serve users? What technical challenges (data quality, model fairness, regulatory compliance) did you face?
US data policies and regulations are well-defined and have been in place for a very long time. Multiple data aggregators help you get data in a structured way for apps like ours. Consumers can link their bank accounts to third-party apps, so data is easy to obtain. to apply good AI/ML to the problem. We provide financial plans for our consumers, and we provide a chatbot for users can talk to and make basic financial decisions—it acts as their own financial advisor. The app has your data, so it can answer those questions in the right privacy protected context—that's how we use AI/ML. We aim to give consumers the right tools to manage their money and build their credit score.
Q. For students interested in ML for finance, what foundational topics (e.g., risk modelling, privacy, robustness) should they focus on during their BITS curriculum or self-study?
They should do two things: learn ML/AI and learn finance. Finance and financial discipline are crucial. Learn how to save and invest your money in mutual funds, how much tax you should be paying, what the stock market is, and how to assess a company. These are the foundations of basic financial literacy. Then you can target problems to solve, using ML tools after learning them simultaneously. Doing basic ML/AI for any domain involves separate problems and needs its own foundations. Once you can integrate that, you can target specific problems. There's no need to target ML for finance, instead focus on ML foundations and financial education
Q. Amidst the rapid development of AI affecting jobs, what should students learn and develop to secure their future prospects?
The world is fundamentally changing rapidly—how LLMs are advancing. Invaluable things to have are the ability to problem-solve and good coding skills. The ability to do criticalproblem-solving and thinking is invaluable when you start using AI. Being able to call out when things go wrong, when the model hallucinates, when responses are largely false, and learning how to solve this is important. The approach should be: get used to the tools, build the habit of critical thinking, ask the right questions, and do good structured problem-solving. These are three invaluable skills that will help you adapt to the changing environment.
Q. Why should engineers/data scientists understand business metrics, unit economics, and investor perspectives? How can BITS students build that business acumen?
Understanding business metrics helps engineers and data scientists solve the right problems, those aligned with customer needs and business goals. Without context, even great tech solutions can be meaningless. Knowing what makes a good product or business helps you build something valuable.
To build this acumen, study real businesses: read case studies, analyze what worked or didn’t, and read quarterly reports. Repeated exposure builds instinct and frameworks to evaluate business value.
Q. What qualities do you look for in new hires? How can BITS students stand out?
The key is problem-solving and first principles thinking: can you explain what you built, why, how, and go deep into it? Strong hires show clarity of thought, first-principles thinking, and the ability to break down and solve problems.
You don’t always need specific tech stacks, what matters is showing you can think clearly and take a project from start to finish.