About

Faraz Zaidi is a skilled Machine Learning Engineer currently working at JP Morgan Chase & Co. He graduated from the prestigious Indian Institute of Technology, Roorkee, with a degree in Data Science. Faraz is passionate about the intersection of Machine Learning and Quantitative Finance, and his expertise includes Tensorflow, PyTorch, PySpark, and SAS. In his current role at JP Morgan Chase, Faraz has designed and developed various scalable and automated data models using Python, Pyspark, and shell scripting. He has also converted code from SAS to Python to take advantage of PySpark's parallel processing engine on a cluster. Faraz has devised a solution to recommend optimal instance fleet configuration for an EMR cluster using Time Series Forecasting and Dynamic Programming, and he has filed a patent for the same. His tool that automatically generates intelligent samples of the tables required for a model has sped up testing models by 30%. Prior to his current role, Faraz worked as a Deep Learning Researcher at Technische Universität Darmstadt, where he approximated the p.d.f. of a source kernel distribution using a small subset of problems from a specific data domain. He also visualized the performance of Deep Ensembles versus Bayesian Neural Nets on standard datasets such as CIFAR-10 and CIFAR-100. Faraz has also worked as a Data Scientist at Classplus, where he extracted questions and their respective options using heuristics and publicly available OCRs such as Mathpix and Textract. Faraz's education history includes an Integrated M.Tech degree in Data Science from IIT Roorkee, which he completed in 2021. His technical skills include ML, Python, Deep Learning, AWS, Reinforcement Learning, XGBoost, AI, and Spark. With 1.86 years of relevant experience, Faraz is a driven and accomplished Machine Learning Engineer who is passionate about using his skills to drive innovation in the field of Quantitative Finance.