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What it's like to have the best job in America right now
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Business Insider interviewed a data science manager at
Glassdoor to learn what it's like to have the best job in
America right now.
Data scientists not only command high salaries, but
play a huge role in influencing company
decision-making.
For the third year in a row, data scientist has been ranked the
best
job in America.
According to rankings by job site
Glassdoor, the data scientist position has the highest
overall job score of 4.8 out of a possible 5.
To determine its
job rankings, Glassdoor takes into account the average salary
of positions listed, the number of open positions, and the
average job satisfaction of employees in these roles.
According to Glassdoor, data
scientists have an
average compensation
of $120,000 per year, there are
4,524 job openings, and overall job satisfaction in the position
scores a 4.2 out of 5.
To find out what it's really like
to have the best job in the country, Business Insider interviewed
Ling Cheng, a Data Science Manager at Glassdoor.
Why data scientist is the best job this year
Aside from the great pay and
ample job opportunity, data scientists help steer their companies
in the right direction.
"I think that it's a really
exciting field," Cheng told Business Insider. "More and more
companies are starting to realize the potential they have in
their data. A data
scientist who's answering questions for you does their own
delving in and serves as the detective.
"You have to be open to what
comes out of the data. What is it telling you versus what you
believe in? This can
happen when we look at A/B tests, when we look at products, and
it can be in strategy, where we find what's working and what's
not."
What being a data scientist actually looks like
Data scientists can impact the
decisions that managers make, with respect to product management
and operational efficiency.
"It varies by company, but
overall, it could be generating insights for high-level
decisions, product decisions, business decisions, or strategy
decisions," Cheng said.
The job can also include building
dashboards to display data in a more visually clear way. A single
dashboard can include several charts and graphs containing
different information.
"In Glassdoor's case, it could be
building salary estimates, looking at how to return results for
job search," Cheng said. "I think those are kind of the big
areas. Insights, dashboards, and building products."
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The main difference between a data scientist and a data engineer
Although there is quite a lot of
overlap between data scientists and data engineers, their roles
are not to be confused.
"A data engineer is responsible
for making sure that data scientists have all the data that we
need and that we get it in a timely manner," said Cheng.
As Vik Paruchuri wrote on
DataQuest, "Data engineers are responsible for constructing
data pipelines and often have to use complex tools and techniques
to handle data at scale." He continued: "Unlike [data science],
data engineering leans a lot more towards a software development
skill set."
Data scientists really depend on
data engineers, Cheng told Business Insider, "because we need the
data processed, and we need it available in a way that we can get
to it without waiting hours. So they build tools and process the
data in a way that allows us to do that."
A typical day in the life of a data scientist
Like many jobs, each day in the life of a data scientist can look
extremely different.
"It's a combination of coding,
reading papers, talking to product managers, data engineers, and
then also looking at data myself to get some of my own insights,"
said Cheng.
"But mainly, each day is
different. This morning, I was reading a paper that someone
shared, and then I had to interpret it and see how well the ideas
could work for what we're doing.
"And then I had to look at some
code that someone was prototyping. So I'm looking at their code,
how it performs, and then I have to talk to so many different
people to get it working.
"So we work with data engineers
for implementation, I work with the product managers to discuss
the products at hand, and because I'm a manager, I also talk to
my reports."
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Misconceptions about the job
"One of the misconceptions is that data scientists are just
datamonkeys, that others in the company come up with all the
questions that we need to answer, and you have a bunch of
requests, and they are just going to answer your question and
come back to you with that," said Cheng .
Instead, data scientists can
impact the decisions that managers make with respect to product
management and operational efficiency.
"With almost every project, as we
start digging through the data, we find new directions to go to,
or maybe we find things we didn't know were there, or maybe the
outcome was totally different from what we expected.
"You're not just answering
questions given to you by the managers, which I think is
sometimes what people might think to hire one for," she said.
Most useful skills for a data scientist to have
There are both technical and
entrepreneurial skills aspiring data scientists should develop in
order to succeed in the role. Cheng outlined a few:
• SQL • Some Python • R
• Modeling
• Dashboarding
• Depending on the data scientist you are you can use
Tableau
• More recent big data tools like Hadoop
"Just as important is having
product and business sense," Cheng said. "Having your own
intuition and understanding of your subject area so that you can
come up with good questions and build models that actually make
sense to the area that you're building."
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How to become a data scientist
The level of education you need
to become a data scientist
can vary depending on the role and company at hand.
"More analytics-focused DS roles
typically just need a bachelor's degree, but ML-focused roles
tend to prefer either advanced degrees or more experience," said
Cheng.
In her experience, many PhD
graduates in STEM fields become data scientists, because
DS-focused roles generally pays better, and come with research
and learning aspects that appeal to PhDs. There's
even a program
called Insight Data Science that specifically trains PhD graduates to
become data scientists - but although Cheng works with many PhDs,
she says you don't need one to be successful in the field.
"Also, there are a lot of data
science openings and there are a lot of people training to become
data scientists," Cheng said. "If you are totally getting into
it, there are data science bootcamps, there's online courses,
there's Udacity, there's online communities."
Advice for breaking into the industry
"I think the way to build the
product and business sense is to build projects using real data,"
Cheng said. "I always tell people who have no experience to find
something they're interested in and find a way to do a data
science project around that."
One of the more well-known
examples of someone who created their own project is Nate Silver,
who started posting forecasts for the performance of MLB players
for fun. He became more
famous after predicting the individual state outcomes of 2008
Presidential election, Cheng said, and then started the fivethirtyeight blog, which
gave a higher probability to Trump winning the 2016 election than
most sites.
"You can even do it with any job,
even if you're not a data scientist," Cheng said. "What I've seen
is people in operations doing some data science on their area and
that can actually transition you into a data scientist. It's
finding ways to be a data scientist in your current job."
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