Are Data Scientists the ‘New’ Rockstars?

Are Data Scientists the ‘New’ Rockstars?

Daniel Hill, a research analyst for the global equity team at William Blair, has written an insightful piece about the hot competition for data scientists underway in the alpha-seeking world today. 

Hill begins with the observation that there are lots of different buzzwords, hashtag-worthy words and phrases, at use in the asset management world today that refer to data science in one way or another. There is “Big Data,” of course, as well as “artificial intelligence” and “machine learning.”

It is perhaps easiest to see the operational results that justify the high salaries data scientists can command by looking at operational businesses like airlines and package delivery. Southwest Airlines saved $100 million a year by integrating data in a way that lets it reduce the amount of time its planes spend sitting idle on a tarmac. Likewise, changes at UPS saved 39 million gallons of fuel a year.

All About Alpha has long chronicled the way in which these trends impact the search for alpha, accelerating the innovations in trading models and making a star out of Renaissance Technologies.

The Best or the Rest?

One important fact, noted by Hill, is that it is hardly worthwhile bringing such talent on staff unless one is willing to bring in the best. There isn’t a lot of room for a second string. In undergrad courses and again in search of a master’s degree, programs on information technology tend to be quite clinical in their approach: they show their students what to do with a stack of clean data. It isn’t until or unless they pursue doctorates that students are pressed to show what they can do even with dirty data.

What this means, Hill says, is that “the demand for doctorate-level data scientists far exceeds universities’ ability to train them, and this supply-demand imbalance shows no sign of abating.” Accordingly, William Blair is developing a granular analysis of data-driven companies that, Hill says, will include a rigorous assessment, company-by-company, or recruitment and retention methods as they impact the quality of the data science.

The International Institute of Business Analysis recently hosted a conference on this issue, “the promise of data and the shortage of data scientists.”

A Brief History of the Field

With all the attention to this shortage, it is perhaps difficult to recall that just 20 years ago the whole category of “data science” was new. The internet, of course, was the catalyst for the growth of the field. Everything one does while using the internet, every click and scroll-down, is trackable and becomes part of somebody’s database. Furthermore, in the 1990s when people were still trying to figure out the basics of this new technology, there was lots of novel research that had to be done into this huge new database of other people’s clicks.

About 15 years ago, LinkedIn developed its “people you may know” feature, which was both an example of the kind of work that had been underway until then and a catalyst for new work. LinkedIn’s algorithm figured out who might know whom from previous jobs, school days, a common hometown, and so forth. It was often disconcerting to have a website tell you that “you may know” your old college flame or even the old flame’s old roommate. It was a bit like the work of a good stage “mentalist.”

Fastest Growing Jobs

According to an analysis prepared for CNBC by Payscale, the top fifteen fastest growing jobs in the US today are: full stack software developer; director of community engagement; lead graphic designer; manager, customer success team; senior mobile developer; employee engagement manager; computer vision engineer; business intelligence engineer; machine-learning engineer; population health manager; commercial truck driver; senior data engineer; site reliability engineer; project management office manager; and data science manager. Those who know how to make use of data, or how to get machines to make use of data, dominate the list, including the first item, the fifteenth, and several in between.

From the rock-star status of the adepts in data science, it follows that a fund firm’s ability to hire and make good use of these adepts is a key differentiating factor in its success: in its ability to find and exploit investment opportunities, and to find and attract investment capital from savvy institutions.

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