I started at Mu Sigma in 2014, spending two years building segmentation, demand forecasting, and marketing mix models for Fortune 500 retail and airline clients. It was a strong foundation in applied analytics - learning how to frame problems, what good questions look like, and how to translate analysis into business action.

I joined Amazon in 2017 and spent nine years there through April 2026. That tenure took me across payments and high-frequency commerce in India — first as a BA, then leading the analytics function — before moving to private brands in Luxembourg, and eventually the selection expansion team, where I was the sole BIE supporting a $11.5B program across 17+ global stores.

What changed for me at Amazon was the scope. I went from building models to building systems - things that needed to run reliably, communicate clearly to many stakeholders, and hold up under business pressure. That experience fundamentally shaped how I think about analytics as infrastructure.

The flagship is Disha Analytics — a live B2B SaaS that turns restaurant data (reviews, menus, competitor maps, owner financials) into structured intelligence reports. Built on a six-agent AI pipeline with a three-tier ingestion architecture. Pilot running in Luxembourg with a real client.

I've also built Soul Spark (multi-agent life intelligence across finance, health, and career), JD Analyzer (resume-to-role fit scoring), Journey Planner (multi-modal travel decisions), and Dota Analyzer (match intelligence). Each one solves a real problem, not a proof-of-concept.

Building these has deepened my understanding of LLM integration, multi-agent architectures, local-first privacy patterns, and the hard work of making AI output genuinely useful — not just technically impressive.