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 stayed for nine years. 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 analytics 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.

I've been independently building AI-powered products that reflect the same design philosophy — data integration, clear decisions, useful output. Soul Spark is the most ambitious of these: a multi-agent life intelligence system that connects finance, health, career, and behavioral signals into a conversational advisor.

I've also shipped JD Analyzer (resume-to-role fit scoring), Journey Planner (multi-modal travel decisions), and Dota Analyzer (match intelligence for competitive players). Each one is a real tool solving a real problem, not a proof-of-concept.

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