When it comes to hot jobs, ‘Data Scientist’ is the top of the heap.
In fact, The Harvard Business Review wrote about this in-demand position as far back as 2012 calling Data Scientist the ‘Sexiest Job of The 21st Century.’
As more and more companies deal with petrabytes of business information and hunt for people to decipher that data cogently and with an eye on their larger business objectives, there has been no greater time than the present to be a data scientist.
LinkedIn voted “statistical analysis and data mining” the top skill that got people hired in 2014 and McKinsey predicts that within the next three years, the U.S. will face a shortage of 140,000 to 190,000 people with analytics capabilities and 1.5 million managers who know how to translate data into business decisions.
Given all this information, if a candidate asks, “I hate math! Can I still be a data scientist?” You may be tempted to give them a resounding NO as an answer.
To fit into a big data job, a person has to enjoy or at least not be intimidated by mathematics, statistics, market research and analysis.
In fact, to be a data scientist, a candidate should be able to perform the following basic functions:
• Parse through large amounts of data that doesn’t necessarily appear row after row in a spreadsheet
• Integrate data points from disparate sources
• Connect the dots on these data points by creating mathematical models that can assess consumer behaviors.
But expert math ability is just part of the story.
Here are some other vital skills that make a killer data scientist package.
1. Strong Interpersonal Skills and The Ability to Work Collaboratively: Unlike software or app development where people often create products or services in a highly independent fashion, big data requires reaching across traditional organizational silos. To do this effectively, companies look for candidates with solid interpersonal skills and who can cogently present data in a business context.
2. Ability to Read Data with Company’s Strategic Objectives in Mind: A great data scientist is someone who can delve into an ocean of chaotic data with a clear understanding of his/her company’s strategic objectives and help the enterprise find critical information they need to make better business decisions. Anyone can crunch numbers, but someone who can analyze data strategically is invaluable.
3. Ability to Innovate: Finally, businesses want innovators who have demonstrated an ability to create and succeed throughout the course of their professional careers.
Any candidate with these essential math and non-math skills can find themselves very competitive for this hot job.
And with a little bit of creativity, companies can also find data scientists in unusual places. For example, in an article for The Wall Street Journal writer Elizabeth Dwoskin profiled a trio of data scientists who all held Ph.D’s and had transitioned from academia to being data scientists in business:
• One was an astrophysics researcher who conducted research on the giant particle accelerator and who now analyzes business ratings for Yelp.
• Another applied biostatics to breast cancer research and is now analyzing fashion terminology for Etsy, a global retail community.
• A third data scientist used a degree in cognitive psychology to analyze how people change their political beliefs. This person now works for Square Inc., a credit card processing firm, to determine which retailers are more likely to have customers who want their money back.
I have had a similar experience in my own recruiting where I have placed many people with diverse backgrounds.
Recently, I helped a Fortune 50 financial services company hire 10 new data scientists into their firm. Of those, one has a Ph.D. from Stanford and was using data-intensive computing to assess brain networks while another was an AP math teacher with a passion for statistics and a master’s degree in data science specialization from Johns Hopkins University.
So, will you like being a data scientist if you don’t like math? The short answer is No.
For tips on where to find and how to recruit data scientists, take a look at my white paper here.
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