Ladies and gentlemen of the analytic community – we have an
epidemic. A seed has been planted in the minds of quantitative professionals
that has sprouted and will not subside. The more we hear about Big Data, the
more we hear about ‘Data Scientists.’ And like with any hot job, once the media
starts to speculate on salary, candidates go nuts. As the President of Teradata
Scott Gnau said in a recent article about careers in Big Data, “There are a lot
of people who can spell Hadoop and put it on their resume and call themselves
data scientists.”
Gnau makes a plea to get the term ‘Data Scientist’ defined
and I could not agree more. When a national article makes the claim that one of
these workers can be making $200K+ with a couple of years of experience, you
can bet everybody who works with data will start calling themselves a Data
Scientist. I have already seen it start to happen and thought I would offer my
two cents on the reality of this highly coveted (and grossly overused) title.
The compensation issue is a tough one to tackle. Probably
the most common question I get asked as a recruiter, by both candidates and
clients, is related to salary. While it’s true that careers in marketing
analytics are in demand and lucrative, this is all dependent on multiple
factors including: years of experience; software familiarity; advanced degrees;
pedigree of school; location preferred; and many more. So when Rob Bearden, the CEO of Hortonworks,
says that a “qualified data analyst” coming right out of school can make $125K,
note that this is a very specific candidate with a very impressive background.
To name just a few bullet points of the perfect candidate, data scientists
typically have:
- A PhD in Computer Science and advanced degrees in other highly quantitative disciplines like Mathematics
- Knowledge of MapReduce tools like Hadoop
- Use of tools such as Python, R, Java, Hive, Pig
- Prior work or internship experience working with enormous, unstructured data sets
These are just general guidelines, of course. To be sure, a
candidate with a Masters in Statistics can be a Data Scientist but this
candidate will likely not be demanding as high of a salary as someone with two
PhDs and Google on his resume. It might help to think of Data Scientists as a
unique subset of the Big Data analytics profession. Just as an IT professional
may work with Big Data, a marketing analyst who builds predictive models is not
necessarily a Data Scientist.
Allow me to reiterate what I’m sure you have already heard
countless times in the media: careers in Big Data are in demand and continue to
grow. However, not every person with a quantitative background or experience in
analytics is a real Data Scientist. The tools they are using are very new and
many companies looking to take advantage of Big Data do not have a previously
established foundation of Data Scientists to provide road maps for less
experienced professionals. Likewise, innovative companies like Hortonworks that
work to support tools such as Hive and HBase can and must offer workers
competitive salaries to ensure the use of these systems are further developed.
For now we will continue to see ‘Data Scientist’ placed on
resumes to catch the attention of hiring managers, and hiring managers will
continue to shell out unprecedented resources to attract talent. Rightfully so
– these guys are one in a million.
