Ian Laker (MFE ’17)
Machine Learning Engineer at Apple
Austin, Texas
“In quantitative research, it's imperative to think of questions that no one would and to have different points of view. The technical skills are a must, as there is a ton of data that you would have to sift through before getting the signal you want. What I like most about this role, however, is coming up with ideas and looking for factors that no one has thought about. Because we look at alternative data sets that a lot of shops have not considered, there is not a ton of previous work done, so we have to think outside the box and make decisions subjectively. You work all the way from idea generation to data gathering to portfolio analysis to deployment in the actual models, which I think is really cool.
Quant research typically involves building models that identify latent characteristics of different financial instruments and identify strategies that are designed to take advantage of the differences in these attributes. The primary goal is to create trading strategies that result in outperformance on a risk-adjusted basis. Once these are identified, you are tasked with running a backtest to see how said strategy would have performed in the past. As you come up with new ideas, it is ideal for the newly found and the currently existing strategies to have a low or negative correlation. There needs to be a clear balance between return and risk, and several robustness checks need to be met before employing these strategies in the real world."
