Abstract: 

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions.
Machine learning techniques are now being increasingly used by financial firms for generating profitable trading strategies and for automating various processes. Quants typically have background in hard sciences and mathematical finance. In addition to classical techniques like derivatives modeling, asset allocation etc. quants now need a good understanding of machine learning models and statistical methods. Experience in deep learning techniques for text and image processing is required for handling unstructured datasets (also called alternative data).

Data scientists transitioning into quant finance should develop a solid foundation in financial concepts and business knowledge. Data visualization and tools to explain how the models work under the hood are also crucial. Python is becoming the language of choice for scientific computing and machine learning. Data scientists should develop strong computing skills with focus on data analysis, storage and handling of unstructured datasets.

Bio: 

Chakri Cherukuri is a senior researcher in the Quantitative Financial Research Group at Bloomberg LP in NYC. His research interests include quantitative portfolio management, algorithmic trading strategies, and applied machine learning. He has extensive experience in scientific computing and software development. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. He holds an undergraduate degree in mechanical engineering from the Indian Institute of Technology (IIT) Madras, India, and an MS in computational finance from Carnegie Mellon University.