Uncharted Career: From Data Scientist to Tech Founder, and Everything in Between

I've had a unique and, honestly, quite a chaotic journey in my career so far. My path, starting with economics at IIT Kanpur, then transitioning to data science in banking, and eventually finding myself in the tech startup world, has been filled with surprising turns, unforeseen challenges, and plenty of moments of self-doubt.

The Academic Foundation

At IIT Kanpur, my academic journey was eclectic. Economics was my primary subject, but thanks to the flexibility of IIT Kanpur's course structure and broad electives, I explored far and wide. I ventured into computer science, doing enough credits for a minor. The courses around artificial intelligence captivated my interest: an early glimpse into my future tech-oriented career.

I also explored humanities subjects like Psychology and English Literature. This broad academic exploration enriched my perspective and built a diverse knowledge base that has consistently informed my approach to challenges.

The First Startup Bug

Amidst all this academic exploration, the entrepreneurial bug bit me. Along with my friends, I co-founded a startup called Yatayat: an Uber for auto-rickshaws in Kanpur and Delhi. Our idea got selected for an incubator program in Helsinki. The joy of seeing our idea taking shape was unmatched.

However, life is full of tough decisions. I found myself at a crossroads when an offer from Citi came along. In retrospect, choosing the security of a corporate job over the excitement of a promising startup may not have been the best decision. Still, it was a valuable lesson about the choices we make and the directions they can lead us.

The Data Science Years

After graduation, I joined Citi as a data scientist. It was intense: demanding tasks that challenged me intellectually. I appreciated the complexity of the work and was eager to put my economics training to good use.

The project that taught me the most was analyzing spending patterns. By clustering transactions, we could infer when and where a user was most likely to transact next, and for what. Spending concentrated near home and office at predictable times. From this data alone, you could build surprisingly accurate predictions about behavior.

That was a formative early-career lesson: data is powerful. Not just for measurement, but for understanding the rhythms of how people actually live.

But as I worked through piles of data and grappled with financial models, I couldn't shake the feeling that I was a small cog in a big machine. The experience taught me data-driven decision making at scale. Working on merchant offers, network analytics, and emerging payments gave me a front-row seat to how large institutions think about risk, opportunity, and optimization.

The Startup Calling

Despite my initial interest in finance, I felt drawn to the world of tech startups. This led me to join Credibase (now known as B2Brain) as a founding principal engineer in 2013. Suddenly, I was in a less structured, more dynamic environment where I had to play multiple roles.

At Credibase, I learned what it meant to build something from scratch. Every decision mattered. Every line of code had immediate impact. The feedback loops were tight, and the learning curve was steep.

Building Balance

Then came the opportunity to start my own venture. In 2015, I founded Balance, a fintech startup that aimed to help everyday users save money by identifying micro saving opportunities.

We built India's first micro-savings app that recommended saving opportunities based on spending patterns, salary timing, fitness goals, and even favorite sports events. The idea was simple: saving shouldn't feel like punishment. It should seamlessly integrate with life's joys.

Often, I questioned if I was in over my head, but each challenge taught me valuable lessons. We raised funding, built a team, and most importantly, built something people actually used.

The Paytm Chapter

In 2018, Balance was acquired by Paytm. The feeling was bittersweet. There was validation-proof that what we'd built had value. But there was also an identity shift. I went from being an in-control founder to figuring out how to get things done in a chaotic, multi-stakeholder corporate environment.

It took time. Many lessons learned on how to operate at scale with large teams and many stakeholders. Logic doesn't always work in such situations. Politics, relationships, and organizational dynamics matter as much as the right answer.

Over 3.5 years at Paytm:

• Led Customer Service product team, automating and improving user query UX, driving down manual handling by more than 30%
• In Payments Gateway team, built merchant gift vouchers as a new payment instrument
• Launched Mini Apps product, helping merchants leverage the Paytm platform
• In UPI platform product, built features like AutoPay, UPI Number, and UDIR

Working at national scale taught me things you can't learn in smaller environments. The complexity of serving millions of users daily and the organizational dynamics made it a masterclass in building at scale.

The Healthcare AI Pivot

After Paytm, I explored AI applications in physiotherapy and clinical workflow automation through PiMotion Research. This was my first deep dive into healthcare technology, and it opened my eyes to a sector in need of thoughtful innovation.

Today, I'm building EasySLR: AI tooling for systematic literature reviews in healthcare and pharma. It's a process that traditionally takes research teams months of manual work: screening thousands of papers, extracting data, synthesizing findings. We're building AI that can augment this work while maintaining the rigor and auditability that healthcare demands.

The Lessons

When I reflect on my journey, from my early days as an economics student to my current role building AI in healthcare, it seems far from straightforward. I've faced uncertainty, made risky pivots, and often wondered if I was making the right choices.

A few things I've learned along the way:

1. Breadth creates serendipity. My diverse academic background (economics, CS, humanities) created unexpected connections. The spending pattern analysis at Citi informed how I thought about user behavior at Balance. The behavioral economics from college showed up in product design at Paytm.

2. The startup itch doesn't go away. Once you've tasted building something from zero, the pull to do it again is strong. Better to embrace it than fight it.

3. Scale teaches different lessons. Working at Paytm taught me things I never could have learned at a startup. Both experiences are valuable for different reasons.

4. Healthcare is hard for a reason. The stakes are higher, the regulations tighter, the trust harder to earn. But that's exactly why it needs good technologists.

5. Career paths don't have to make sense in the moment. Looking back, I can connect the dots. Looking forward, it was mostly faith and curiosity.

If there's a through-line in all of this, it's that I keep choosing problems where correctness matters and incentives are messy. That's why healthcare AI feels like the right kind of hard: it forces rigor, not hype. Even now, I can't claim to know exactly what the future holds for me. But what I do know is that every new challenge, every unexpected turn, brings me closer to finding my place. The journey continues.