I'm Aadarsh — currently Head of Analytics at Airtm, where I lead the evolution of our quantitative and data functions to drive efficiency across a global marketplace. Before this, I spent a few years as a Quantitative Developer at Airtm, architecting systematic frameworks for pricing, matching, and liquidity sourcing across complex currency corridors — turning messy market dynamics into scalable, data-driven strategy.

My path here runs through institutional finance: I started at Goldman Sachs as an analyst, then spent time at UBS and BMO Capital Markets in global markets and trading roles in New York. That stretch gave me a grounding in how markets actually work under pressure, which I've since paired with a deeper focus on machine learning and quantitative modeling.

I hold a Master's in Financial Engineering from the Haas School of Business at UC Berkeley, and a Bachelor's in Electronics Engineering from IIT (BHU) Varanasi. Along the way I picked up a Machine Learning Engineer Nanodegree (Udacity) and the Deep Learning Specialization (Coursera) — mostly out of curiosity about where classical quant methods end and modern ML begins.

This site is where I put that curiosity to use outside of work: experiments in Python, machine learning, algorithmic trading ideas, and general notes on quantitative finance and data science. Think of it as a running notebook rather than a polished portfolio — I'll post things as I learn them, not just after I've mastered them.

You can find me on LinkedIn or GitHub.