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.
In addition, we can conclude that this is
a job for highly educated people.
Of course, there is the occasional exception
to the rule, but three out of four data scientists
in the cohort held a Master’s or a PhD degree.
Indeed, data science is a profession that
requires strong academic background.
However, given that this is a relatively new
field, it comes as no surprise that the data
scientists included in the study have heterogeneous
academic profiles.
Degrees such as Computer Science, Statistics
and Mathematics, Economics and Social Sciences,
Data Science and Analysis, Natural Sciences,
and Engineering dominated the field with 91%
of the professionals having graduated from
one of them.
The conclusion?
Universities and colleges still struggle to
meet the growing job market demand for data
scientists and companies hire intelligent
candidates with different backgrounds.
These people have probably been able to acquire
the skills employers look for on their own
through self-preparation or through extensive
on-the-job training.
How can one self-prepare to become a data
scientist?
Some of the most popular online courses teach
people how to run machine learning algorithms
in Python and R, and how to deal with databases.
E-learning is definitely a resource many data
scientists take advantage of.
The study shows that 40% of data scientists
have posted an online certificate on their
LinkedIn profile, and the average number of
certificates per person is 3.
Is this a job for people coming from top tier
universities only?
It isn’t, actually.
Yes, more than 28% of data scientists came
from top tier universities (top 50 in the
“Times Higher Education” world university
ranking), but a significant portion of professionals
(more than 25%) graduated from schools that
were not even included in the ranking or were
ranked after the 1000th place.
So, if you are an aspiring data scientist
who is about to graduate (or has graduated
from) a non-target school, you shouldn’t
worry too much – you still have significant
chances of landing the job.
Self-preparation looks like the key to success
in the current environment.
Which are the industries hiring the most data
scientists?
It has got to be the Tech/IT industry, right?
Indeed, it is.
Technology companies are seen as a symbol
of innovation.
Moreover, data science is essential for such
firms as it helps them read online behavior
patterns, understand customers’ desires,
analyze online search, improve product offering,
and so on…
Industrial firms come in second, hiring more
than 37% of data scientists, while the financial
(15%) and healthcare (5%) sectors come in
as third and fourth, respectively.
It gets even more interesting if we dissect
this data by country.
We begin to see that the financial industry
in the UK employs a significantly higher percentage
of data scientists (~20%) with respect to
the other clusters.
And it makes sense: London is known as Europe’s
financial capital and plenty of financial,
trading, and brokerage firms reside there.
The job market in India, on the other hand,
mainly employs data scientists in the Tech/IT
sector.
This is coherent with the country’s status
as the world’s prime destination for outsourcing
of Tech and IT services.
Our conclusion?
Hopefully, this research paints a clearer
picture for you and helps you understand the
core skills and qualifications people currently
employed as data scientists have.
In addition, the country-wise segmentation
is invaluable, as geographical differences
pertain, and so does the skill set required
to land the job.
If you are interested in a solid data science
preparation starting from scratch make sure
you visit our website 365datascience.com where
you can start your preparation and see if
this is the career path for you.
