Abstract: 

More and more quantitative hedge funds are founded every year, as investment capital is removed from 'discretionary' stock-picking funds and allocated to quantitative trading strategies. Traditional quant trading methods--trend-following and mean-reversion--are thus coming under competitive pressure paving the way for so-called 'alternative data' strategies.

In addition the prevalence and maturity of open source data science tools, along with the ever-decreasing costs of cloud compute power are providing these funds with sophisticated capabilities to analyse novel data sets.

This has opened up significant career opportunities for individuals with experience in open source data science and software development. Those with Python/R machine learning skills are in particular demand.

In this talk we will discuss the modern quant fund industry, what it is like to work in a quant fund as well as why you should consider it as a meaningful and highly satisfying career path compared to academic research, technology startups or corporate data science.

Bio: 

Mike received his PhD from Imperial College London where he developed fluid dynamics codes for hypersonic propulsion engines. Subsequent to his PhD he worked as the lead systems developer for Oxalyst Systems LLP, an alpha-capture quant fund based in London. He is now the founder of QuantStart.com, which discusses quantitative trading methods using Python and R.