About me

Hi, I’m Amit. I work as an Authorized Officer (Quantitative Analyst) in the Quantitative Risk Modelling division of UBS Investment Banking, Mumbai. My work spans risk factor model development and backtesting (Local Vol, GBM, QGM, PK, Hull-White) across all major asset classes—Equities, FX, IR, Credit, Basis, and Inflation. I also drive AI-focused solutions at UBS, regulatory remediations, and model implementations, ensuring both innovation and compliance in a highly regulated environment. As part of the Exposure Model Performance Crew, I contribute to enhancing model accuracy, stability, and predictive power through advanced risk analytics. Previously, I worked as a Research Analyst at the Reserve Bank of India (RBI). Academically, I hold a B.Sc. (Hons.) in Physics from Jamia Millia Islamia and a MSc with a Computational Economics & Quantitative Finance Track from IIT Jodhpur. I qualified GATE in Economics (XH) with an All-India Rank of 189 placing in the top 8%. I was also selected for the PhD program in Quantitative Finance at IIT Madras—a recognition of my research potential—though I later stepped away due to personal challenges. My research interests span financial mathematics, risk management, machine learning, and financial economics. Beyond work, I actively contribute to research and open-source projects, including an R package (CustomDerivative) for exotic derivatives pricing and a Python-based stochastic process simulator (StochPathSim). My research includes an extension of the Hull-White interest rate model, published on arXiv, which improved bond pricing accuracy by 14%. I’m fluent in Python, R, with working knowledge of C++, and currently exploring F#. I also have experience with Bloomberg and Reuters Terminals, and working familiarity with Tableau, and Power BI. At my core, I’m passionate about bridging mathematics, finance, and AI to build robust models, solve real-world financial challenges, and push forward the boundaries of risk management.