Welcome to my Portfolio.
Building the future of work through AI, analytics & human insight.
Not a biography. A short field guide to the questions that won't leave me alone — and the practice I've built around them.
I didn't start with a plan. I started with curiosity about why businesses worked the way they did — and a notebook full of questions.
Curiosity came before language for the work.
I didn't start with a plan. I started with a notebook full of questions about why businesses worked the way they did — and a slow, stubborn refusal to take answers at face value. Long before I knew what 'analytics' meant, I was already collecting patterns: which shops on the street were busy at which hour, which neighbours bartered, which conversations changed someone's mind.
Where curiosity met evidence.
The discipline came from necessity. On the retail floor, every inventory call and customer interaction quietly revealed how data shapes business outcomes — and how often the dashboard disagreed with the operator who actually knew the truth. I started reading research papers between shifts. The first Power BI report I built was ugly and life-changing: it made an argument I couldn't make with words. Analytics stopped being a subject. It became a language.
At the seam between people, process and prediction.
Today I'm an MBA candidate in HR & Business Analytics at Nagarjuna Degree College, Bengaluru — four published research papers across IJIRT and SSRN, ten+ certifications across Deloitte, Goldman Sachs, Google AI and Microsoft, and a working practice that lives at the seam between people, process and prediction. I build for the operator first and the deck second. Signal over noise. Numbers with context. Models that survive a Monday morning.
A decade-long bet on human-centered AI.
Over the next decade I want to help write the playbook for human-centered AI inside organizations — research that operators can use on Monday, not just cite in October. Systems that respect the team you'll have in three years, not only the one you have today. If the last decade taught machines to learn, the next one will be about teaching organizations to listen.
An analytics-meets-people thesis, built one degree at a time.
Everything I know about analytics started long before dashboards. It started on the retail floor — where every customer interaction became a lesson in human behavior, every stockout a lesson in systems, and every shift a quiet seminar in business.
A design-led footwear house where every customer expects an experience, not a transaction. Small team, high ticket size, no room for friction.
“The best operations don't look like operations. They look like calm — and calm is engineered, not improvised.”
A high-volume FMCG outlet — hundreds of SKUs, tight margins, walk-in traffic that doesn't stop. End-to-end: billing, audits, people.
“Every variance is a story. Find the story and the number fixes itself.”
“The questions I asked on the retail floor eventually became research questions. That is how this chapter ends — and how the next one begins.”
Continue to research ↓A working research lab, not a list. Featured studies below open in place — the full archive sits beneath, fully searchable.
Three case studies upfront — the full project library sits beneath, fully searchable. Built to scale from six projects to sixty without a redesign.
Not a skills list — an operating system. Every capability shows where it was learned, where it was applied, and how it connects across research, projects, certifications and experience.
Mirrored from LinkedIn · single source of truth
Profile, network, and the work the algorithm decided to share — pulled from one live feed and refreshed automatically.
MBA Candidate · HR & Business Analytics · Researcher in Algorithmic HRM
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Last twelve months of content
The piece that travelled the furthest
Notes on how AI is quietly rewriting the people-decisions stack — and why analytics leaders need to engage with it before policy does.
A pinned insight worth re-reading
A short field note on how I think about algorithmic fairness when the algorithm is the manager.
Refreshes whenever I publish
Notes on how AI is quietly rewriting the people-decisions stack — and why analytics leaders need to engage with it before policy does.
A short field note on how I think about algorithmic fairness when the algorithm is the manager.
Notes on how AI is quietly rewriting the people-decisions stack — and why analytics leaders need to engage with it before policy does.
Updated Jul 22, 2026 · Synchronised from a single live feed.
A curated selection from my personal journal.
More essays, research notes and long-form writing are available on my personal journal.
“I didn't start with a plan. I started with curiosity about why businesses worked the way they did — and a notebook full of questions. The analytics came later. The discipline came from necessity. The direction came from paying attention.”
Balanced full-time work alongside every academic and research milestone — no shortcuts, no inheritance of access.
Studied after shifts. Researched on weekends. Built the habit before the credentials caught up.
Building a five-year foundation, not chasing a quarter. Compounding over performing.
If you've read this far, you already know what I've done. This chapter is the quieter one — about why I keep doing it, and the person who shows up when no one is watching.
I'm a curious kid who never grew out of asking why. I grew up in a household where ambition wasn't loud, but discipline was. The work always came before the reward. I'm still wired that way.
Strip away the degrees, the certifications, the papers — what's left is someone who likes sitting with a hard question longer than most people are comfortable with.
I'm not motivated by titles. I'm motivated by the small click of understanding — that quiet moment when a dataset, a person, or a problem finally makes sense. Most days, that's enough.
I want to do work that outlives the quarter. Work that compounds. Work I'd be proud to put my name on twenty years from now.
Because research is the most honest form of thinking I know. You can't bluff your way through a literature review. The footnotes don't lie. The data either supports the claim or it doesn't.
Research taught me that being wrong on paper is cheaper than being wrong in production. So I rehearse my arguments here first, where the cost of being mistaken is only my own ego.
Because people are the most under-measured variable in business. We measure machines, transactions and revenue with surgical precision, then make decisions about humans on gut and folklore. That asymmetry bothers me.
Analytics is my way of giving people the same care and rigor we give to inventory.
The certainty that I'm wrong about something today, and I haven't found it yet. That sentence used to scare me. Now it's the thing that gets me out of bed.
To build something durable in the people-and-data space. A practice, a product, a body of research — I don't know yet which one. What I do know is that I want to leave the field a little more rigorous than I found it.
Becoming the kind of person who stops learning because he started getting praised. Confident, comfortable, and quietly mediocre. I'd rather stay a little uncertain forever.
Doing work I respect, with people I respect, for problems that matter. That's the whole definition. Anything else is decoration.
A body of work at the seam between people and AI — research, products, and frameworks that make organizations more humane and more honest about what their data actually says.
And, quietly: a life that compounds. Health, family, a few deep friendships, and a craft I'm still excited about at fifty.
“This portfolio tells the story of what I've done. The next chapter is what we'll build together.”
Tell me what you're building.
Currently based in Bengaluru. Available for full-time roles, research collaborations and consulting engagements.