From 7 Degrees to AI: A Nonlinear Career Path
How studying criminology, graphic design, and operations research prepared me for AI better than any bootcamp could. The story of a career that looked scattered until it didn't.
When people ask how I ended up leading AI products, they expect a clean story. That’s not what happened.
I have seven university degrees, and none of them are in AI. Let me walk you through it.
It started with two things at once
I enrolled in Graphic Design and Computer Science simultaneously. Not because I was overachieving, but because they complement each other perfectly. Design taught me how people interact with interfaces. Computer Science taught me how to build what’s behind them. I’ve carried that dual lens ever since: what does the user need, and how do we make it work?
I didn’t want to stay a developer
I’ll be honest. Design and code were great, but I pursued Operations Research and Operations Management because I wanted to lead, not just build. I saw too many talented developers who stayed in execution for their entire career. Deep experts in one language, one framework, one corner of a system. That’s a valid path, but it wasn’t mine.
I wanted to be the person who sees the whole picture. Who defines what we build, not just how. Operations Research gave me the language of optimization, constraints, and decision-making under uncertainty. Over sixteen years later, that’s exactly what AI product leadership requires every single day.
The cybersecurity detour that wasn’t a detour
I wanted to study criminal technology, the kind you see in movies where someone traces a digital footprint and takes down a threat. That career didn’t exist where I was, so I studied Criminalistics and Criminology first, then won a full scholarship from the Government of India to train in cybersecurity at C-DAC alongside professionals from 20 countries. I placed second overall.
That “detour” taught me something critical: every system has vulnerabilities. Today, when I evaluate AI products, I think about adversarial inputs, data poisoning, and misuse. Not from a textbook, but from training that forced me to think like an attacker.
A generalist in a world that rewards specialists
All of that led to a resume that looks scattered until you see the pattern: data science consultant for the EU during COVID, cloud engineer building a demographics product, senior backend developer on a health-tech platform, technical lead for a backend team, product lead for several projects across industries.
I’ve written Python, Java, R, and SQL, worked across Azure, GCP, and AWS, managed Kubernetes deployments, and built data pipelines.
I’m not the world’s best at any one of those things. And that’s the point.
AI doesn’t need another deep specialist. It needs people who can talk to the C-suite about strategy and then walk into a standup and understand why the model isn’t performing. People who can bridge product stakeholders, design, and technical implementation. As one of my former VPs once wrote about me: technical, detail oriented and personable, with a skillset not often found in the tech sector. The kind of person who connects the dots between vision and execution. And this is exactly the kind of profile AI needs right now.
What this means for AI
The biggest barrier to AI adoption isn’t the technology. It’s the gap between the people building it and the people deciding to use it.
Business leaders are buying AI tools they don’t understand, and technical teams are shipping AI features that don’t solve real problems. And almost nobody is asking what happens when the model is wrong, or who’s accountable.
That’s where a nonlinear background becomes an advantage. I can move between a boardroom conversation about strategy and an engineering standup, and I can think about risk because I studied how systems, both human and digital, break. And if your background looks anything like mine, that’s not a weakness.
Your nonlinear background is your edge
If you’re reading this and thinking your career path is too scattered for AI, it’s not. AI needs people who understand healthcare, logistics, finance, law, and education. The person who studied supply chain and then learned machine learning will build better AI for logistics than someone who only knows algorithms.
Your unusual path isn’t a detour. It’s your differentiator.
I’m proof.