I build predictive models, forecasting systems, and Gen-AI applications that move retail, supply-chain, and logistics numbers. 6+ years turning noisy data into decisions — $1.3M in inventory savings, 12% forecast lift, and an ROC-AUC of 0.92 on returns, to name a few.
I lead data science at Impact Analytics, where I designed a Merchandise Financial Planning solution and the Airflow pipeline behind it for a major North American retailer. Before that, I spent five years at Coupa (née Llamasoft) shipping forecasting and supply-chain design models to customers worth $500K–$700K in portfolio value.
My favourite problems sit at the intersection of classical ML, time-series forecasting, and more recently RAG-based Gen-AI. I like models that earn their keep in dollars, not dashboards.
Each tool is a tiny, client-side reproduction of something I've shipped at work — no servers, no tracking, just browser math. Open the dev tools if you want to read the source.
Holt-Winters exponential smoothing on a retail sales series. Tweak level, trend, and seasonality to see the forecast move live.
Two-proportion z-test with p-value, confidence interval, and lift. The same math I used to green-light markdown strategies.
Click to drop "store" points on the canvas, pick k, and watch Lloyd's algorithm iterate to convergence.
A logistic-regression-style scorer for "will this order be returned?" with ROC curve and threshold tuning.
Kirori Mal College, University of Delhi · 2018
Patna Science College · 2016
May 2025 · Driving business impact through AI adoption
Feb 2020 & Mar 2022 · Customer success through collaboration
Sep 2021 — Present · Mentored 100+ aspiring data scientists
Roles, collaborations, a stats question at 2am — whichever. I reply.