Why “Data Scientist” is being called the sexiest job of the 21st century

Hal Varian, chief economist at Google is famous for saying that “the sexiest job in the next 10 years will be statisticians.” In 2009, we couldn’t have know just how right he was about to become – with one tiny difference: they’re called Data Scientists now. And they’re about to take over the world…

What is Data Science/Data Scientists?

According to NYU data science is all about “using automated methods to analyze massive amounts of data and to extract knowledge from them”. Essentially, data science is about using the massive amount of data organizations are collecting to gain new insights, identify trends and find ways to streamline business practices. When you consider that in 2020 the world will generate 50x more data than it did in 2011, it’s no surprise that an entire discipline has grown up to help people make sense of it.

The term Data Scientist, other than meaning someone who does data science, is a revolution and combination of two distinct roles in an organization. It’s a sort of hodgepodge job that encoumpasses the traditional business analysts, statistician and in some cases programming. To become a data scientist you need a solid foundation in computer science, modeling, statistics, analytics and math. What sets them apart from traditional job titles is an understanding of business processes and an ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.

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Anjul Bhambhi, VP of Big Data products at IBM put it best when he said:

A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.

Why the Demand has Risen

The demand for good data scientists has risen in large part because the big data movement has become mainstream. Businesses are increasingly looking for ways to use the massive amounts of data they collecting and storing to gain new insights. According to a report by Accenture, 87% of enterprises believe big data analytics will redefine the competitive landscape of their industries within the next three years. And 89% believe that companies without a big data analytics strategy in the next year risk losing market share and will not be as competitive.

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The importance of data analytics is reflected in their spending habits. A whopping 73% of companies claim to be spending more than 20% of their technology budget on big data analytics. They’re using this money to invest in both the technology resources and the human ones with a view to increase profitability, gain a competitive advantage and improve environmental safety.

The problem, is that while good data talent is recognized as being critical to benefiting from data analysis, around 4 in 10 companies say their teams lack the appropriate skills. In their report, Forrester Research summed the problem up nicely saying “Businesses are drowning in data but starving for insights.” It’s no surprise then, that the demand for data scientists has grown so rapidly.

What Data Scientists can expect

Mathew Chacko, an IT executive at Coca-Cola, described his need for data scientists beautifully when he said:

We need people who are interested in data discovery — really willing to work with messy data and different sets of data — to find insights and create recommendation engines or predictor models that can have a life of their own. I would love to have that capability within the organization.

The role

The job responsibilities of a data scientist vary widely from sector to sector and even from company to company within those sectors. In general though, the data scientist’s role is to sift through all the incoming data streams (both internal and external) with the goal of discovering new insights. They then need to turn those insights into recommendations for gaining a competitive advantage or solving a pressing business problem.

Today’s data science goes far beyond simply collecting and reporting on data. Data scientists need to not only question and explore existing assumptions and processes, but also be able to communicate their findings and recommendations in ways that the orgaization’s leadership can understand and act on.

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The compensation

The sudden increase in demand for data scientists has created an incredible skills gap. McKinsey estimates that by 2018, the U.S. economy will have a shortage of 140,000 to 190,000 people with analytical expertise. This shortage means that good data scientists are able to demand top dollar for their services.

The NY Times reported that salaries for entry level data scientists had risen to $91,000 nationally and $110,000 in Silicon Valley. More experienced data scientists can expect upwards of $250,000 plus bonuses. In fact, according to glassdoor, data science is currently the 15th highest paid job in America.

With so many companies fighting over talent and salaries on the rise, it’s no surprise that in a recent infographic LinkedIn sited Data Scientist as 5th fastest growing job title in 2013.

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How you can add a little more data science to your role

If, like many of us, data science simply wasn’t around when you were at University, don’t worry! There are still plenty of ways you can garner some much needed data skills.

Go back to school

More than 70 top Universities across the US have started offering Masters Degrees in Data Science. These one and two year programs will help you to bridge the big data talent gap. Many schools will tailor their offerings to target specific fields such as marketing, while others teach more analytical skills like R and Python. Here’s a handy breakdown on Information Week on the Top 20 Programs.

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Take a crash course

If you don’t have the time or finances to do something quite as drastic as getting a masters degree, there are plenty of free and paid for online and in person courses which promise to give you a crash course in Data Science, R, Python, SQL and more. General Assembly offers fantastic full and part-time courses on Data Analysis and Udemy is always a go-to source for accessible online learning.

Attend a meetup

Still not sure about data science or just want to learn more about it and chat to like minded enthusiasts? Go to a meetup! Meetups are a great way to dip your toe in the water without committing too much time or effort. A simple search on Meetup or Eventbrite should give you plenty to chose from. If you’re not ready to meet the data science community face-to-face just yet, jump on the Reddit Machine Learning Subreddit – it has more than 30,000 members!

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Read all about it

There are literally 100s of blogs and books on data science, from the highly technical to the simply interesting data stories. Here’s a few of our personal favorites…

  • Flowing Data – run by Dr. Nathan Yau, PhD, it has tutorials, visualizations, resources, book recommendations and humorous discussions on challenges faced by the industry
  • FiveThirtyEight – run by data-wiz Nate Silver, it offers data analysis on popular news topics in politics, culture, sports and economics
  • Edwin Chen – the self-named blog from the head data scientist at Dropbox, this blog offers hand-on tips for using algorithms and analysis
  • Data Science Weekly – for the latest news in data science, this is the ultimate email newsletter

Watch a video

Reading not your style? There are loads of great talks from experts on data science and its impact. Data Science Central is a pretty comprehensive online resource for anyone interested in Big Data. It you’re just starting out, these TedX talks showcased on Data Science 101 are a great place to start. While you’re there have a look at their infographic on the 8 Steps to Becoming a Data Scientist!

The Sexiest Job of the 21st Century

If “sexy” means having rare qualities that are much in demand, data scientists are the sexiest people around. The market for good data scientists is highly competitive, making them difficult and expensive to hire and even more difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills – yet.

It’s similar to the quant shortage in the 80’s and 90’s when big banks were paying big bucks for anyone with physics and math skills. Eventually, with the rise of University programs teaching data science, the supply of data scientists will catch up with the demand and salaries will level out. But in the meantime – stay sexy guys!


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