Every decade has its hottest job opportunities. During the 1980s and early 1990s people were
in a rush to apply for investment banking jobs. Then, in the late 1990s and early 2000s, it
became clear that the Internet will soon change the world and a lot of tech savvy graduates
started specializing in software and web development. Today, it is ever clearer that big data, machine
learning, and artificial intelligence will become (and in some ways already are) the
key success factor that will determine whether businesses will be successful or not in the
coming years. That said, it comes as no surprise that the
hottest opportunity on the job market in 2017 and 2018 is the data scientist profession. The title “data scientist” sounds sophisticated
and scares off people, but perhaps dissecting the typical profile of these professionals
will help us show you they are, in fact, human, and if you were so inclined, you too could
embark on the journey of becoming a data scientist. Certainly, at a glance the title “data scientist”
has an air of sophistication and pretense, but the data begs to differ. Crunching the numbers, it becomes obvious
that there are traits data scientists share.
To gain a better understanding of the typical
data scientist profile, our team collected information from the LinkedIn profiles of
1,001 data scientist professionals. Unlike previous publications, the primary
source of data we used were not job ads, which skew findings towards the employers’ point
of view. Instead, we relied on information posted by
data scientists themselves. The underlying assumption was that one’s
LinkedIn profile is a good estimator of their resume. Then we proceeded to assign company and country
quotas to limit bias. The cohort was divided into two groups depending
on whether a person was employed by a Fortune 500 Company or not.
In addition, the sample involved data scientists
working in the US (around 40% of our sample), UK (another 30%), India (accounting for 15%),
and other countries (the remaining 15%). Convenience sampling was used, due to limited
data accessibility. Once we gathered the numbers, we stumbled
upon several interesting findings. The typical data scientist profile looks is
a male, who speaks one foreign language, with four and a half years of overall work experience
(this is a median). He works with R and/or Python, and holds a
Master’s and/or a PhD degree. Just from this simple overview, we get several
noteworthy insights: You can be promoted to data scientist fairly
quickly. Assuming you graduate your Master’s before
turning 25, or your PhD before 30, a conservative estimate is that by the age of 30 to 35 you
can expect to be a professional whose job title reads “data scientist”.
Another interesting finding is that R and
Python are on the rise. Previous research shows that the two programming
languages are increasing in popularity in the data science world, and that this is happening
at the expense of other languages like Java and C/C++. The results observed here corroborate this
trend. You need to start learning R and Python if
you want to become a data scientist in 2018.
