Arvind Jha

Background

About Arvind Jha

Quantitative Strategist in the global markets space at Morgan Stanley, where I develop and implement strategies across derivatives and linear products, with a focus on fixed income instruments. My work involves systematic model design, backtesting, and building infrastructure that bridges quant research with production-grade systems. Previously at Goldman Sachs, I was part of the capital liquidity modeling team, where I enhanced internal liquidity models used for regulatory and capital planning. I focused on optimizing capital allocation and improving the interpretability of firm-level trade impacts, helping business units better understand and manage internal funding constraints. During my time at IIT Kharagpur, I was a core member of the Autonomous Ground Vehicle (AGV) student research group, where I developed and deployed deep learning-based computer vision systems for real-time object detection and navigation. This hands-on experience with applied machine learning continues to shape how I approach large-scale, data-driven problems in financial markets. I enjoy working at the intersection of markets, data, and engineering; building clean, robust systems that drive better decisions. With a strong foundation in quantitative modeling, programming, and market understanding, I aim to contribute to the evolving landscape of trading and investment strategy. I’ve cleared CFA Level II and am particularly interested in how automation and AI can reshape the future of the world we live in.