What makes a job sexy? Money, power, sex appeal? To us, finding a job you love, that is totally in demand (read: people want you), is what’s sexy. With that definition, there is no sexier job these days than….data scientist.
According to IBM, we create 2.5 quintillion bytes of data every day. This data comes from all sorts of places; social media, e-mail, purchase transactions, digital photos and videos, and log data (data extracted from web servers), just to name a few. This data is too large and complex to handle with traditional means of data management, and data scientists are the individuals who know what to do with it.
The Role of the Data Scientist
The “data scientist” title has only been around for a few years; it was coined in 2008 by Jeff Hammerbacher and D.J. Patil, who at the time were working on data science teams at Facebook and LinkedIn, respectively. Of course, individuals working in data and analytics have been practicing data science for a while—but back then, it was called Statistics. The difference? An article on Priceonomics sums it up nicely:
Statistics was primarily developed to help people deal with pre-computer data problems like testing the impact of fertilizer in agriculture, or figuring out the accuracy of an estimate from a small sample. Data science emphasizes the data problems of the 21st Century, like accessing information from large databases, writing code to manipulate data, and visualizing data.
So just what does a data scientist do? To put it simply, a data scientist explores and creates tools and methods for wrangling data, analyzes that data, and recommends ways to apply it. A data scientist therefore not only needs to be able to track data, they also need to be insightful, inquisitive, and analytical in order to identify trends, recognize blind spots, determine needs, and make recommendations and decisions that will increase the value of a business.
An example of data science in practice? John Foreman is the Chief Data Scientist at MailChimp, an e-mail marketing service provider. By collecting and analyzing data, Foreman discovered that the idea of “optimal send time” — i.e., the time when a reader is most likely to see and read an e-mail — is a myth. There is no one-size-fits-all optimal send time because humans are complex, and the best time to send an e-mail will depend on the sender, the reader, and the content. To deal with this problem, MailChimp built a tool called Send Time Optimization. The tool recommends the optimal send time for individual users based on click data that it collects. That’s data science in practice.
Who are Data Scientists?
Data scientist roles are usually more advanced than other data roles, and candidates typically have a background in computer science, mathematics, or engineering, according to a Burtch Works 2013 study.
However, as demand grows for individuals who understand data, the line between different data roles is becoming blurrier. For instance, the data analyst’s role, depending on the company, is slowly merging with the data scientist.
Along with a deep understanding of computer science and math, data scientists also need to have good business sense, strong analytical skills, and the ability to communicate effectively. These individuals are not sitting alone in a dark room with a glowing monitor crunching numbers; they are major influencers in a business, and as such play an active role.
Why Data Science is a Promising Career
Data is a huge career opportunity. As the amount of data we create each day grows, so too does the need for individuals who can make sense of it. But right now, there aren’t enough candidates. According to a 2011 report by the McKinsey Global Institute, there will be a shortage of data talent by 2018. The report indicates that “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
Scarcity of talent is one good reason to consider a career in data science, but there’s also the pay; according to a Burtch Works 2014 Data Science Salary Study, data scientists earn a median salary that can be up to 40 percent higher than other big data roles at the same level. The median salary for data scientists with 0-3 years of experience is $80,000, and those with 9 or more years of experience are bringing in $150,000. Those numbers go up when we talk about managerial data science roles; individuals managing a team of 1-3 are earning $140,000 and those managing 10 or more employees are earning $235,000.
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