About

Harsh Tuli is a highly skilled Financial Engineer with a passion for applied mathematics, statistics, and algorithmic thinking. He recently graduated with a Masters in Financial Engineering from Columbia University and holds an undergraduate degree in Computer Science Engineering. His ambition is to pursue a career in Quantitative Finance, leveraging his key strengths in statistical modelling, data analysis, simulation, programming, and problem-solving. Harsh is a flexible and competitive team player who is result-oriented. He is skilled in Python, R, Machine Learning, C++, and SQL. During his academic tenure, he covered topics such as Statistical and Time Series Analysis, Algorithmic Trading, Stochastic Calculus, Optimization, and Machine Learning. Harsh has completed several projects, including Portfolio Optimization using Machine Learning and Trading System Implementation with back-testing capabilities. He also worked as a Financial Engineer at AmeriHome Mortgage Company, LLC, where he gained relevant experience in the field. Previously, Harsh worked as an Intern at Philips Global Business Services and as an Engineering Intern with the CDI (China Digital Innovation) team at Philips. During his internships, he wrote SQL queries to obtain data, digitized the daily management board by creating an app on Microsoft Sharepoint, and analyzed and created reports on business performance using Power BI. Harsh's education history includes a Masters in Financial Engineering from Columbia University and a Bachelor of Technology in Computer Science from R.V. College of Engineering, Bangalore. In addition to his technical skills, Harsh is a quick learner, an excellent communicator, and possesses strong analytical and problem-solving abilities. He is a valuable asset to any team and is eager to contribute his expertise to the field of Quantitative Finance.