Interest in data science has grown tremendously over the last five years as the associated roles have become more established and their value has become more evident. Experts in this field are in notoriously high demand, so when your company decides to take Big Data to the next level, it pays to understand how to tell if you’re making a quality hire.
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There’s a vast amount of money tied into the success of Big Data. And every self-proclaimed marketing data scientist wants you to believe that they’re the genius who will be able to tease industry-changing information from a set of numbers and some code. To be sure; some of them can. But not all will be as successful, and discerning between the two is a substantial challenge.
If you’re ready to hire a data scientist to augment and optimize your marketing and business intelligence, there are some important questions to ask yourself to make sure you get the right person for the job:
Related Reading: Why Your Marketing Needs a Data Scientist, Yesterday
Are They Better at Communicating With Machines, or People (And Which is More Important for You)?
There are two main types of Big Data analytics experts: those whose end user is typically a computer, and those whose end users are primarily human. All good analytics experts should be able to do a bit of both, of course, but most roles end up serving more of one audience than the other.
For example: if your desired end result is a machine learning algorithm to choose which ads to show on a website or make automatic stock trades, your analytics are for computers. If, on the other hand, a person will be making decisions based on the analytics, your data scientist needs a different set of skills; chiefly, strong data visualization and storytelling abilities. Examine the candidate’s work experience to see where their previous jobs have been targeted.
Do They Have a Demonstrable Track Record of Success?
As with any position, you hope to see real-world examples of when they successfully implemented improvements to a business process. In a field full of number crunchers and trend watchers, you would think that this would be easy. However, as data scientist recruiters we often find that even very experienced talent isn’t always able to point to concrete examples of how they made a difference at their organization.
Can They Work with a Team of Marketers?
Stereotypes might lead you to believe that it’s OK for scientists and techie types to be introverts with poor communications skills, but that’s not really an option for a modern data scientist. Today’s Big Data analytics professionals must be able to seamlessly collaborate with their marketing colleagues and translate their data-based information into meaningful, broadly comprehensible insights.
He or she needs to be able to communicate effectively with people who don’t “speak the same language,” tell a story through data, and fully understand the marketing needs of their team to accurately focus their own analytics efforts.
Watch it: LinkedIn’s Daniel Tunkelang Explains How to Interview a Data Scientist
Are They Creative as They are Analytical?
Big data is a rapidly changing and expanding field, making data science recruitment particularly difficult. Long-term success requires a certain open-mindedness and creativity. To innovate, a good data scientist must be able to look beyond what came before. If a candidate has implemented the same processes or procedures at multiple companies, ask yourself seriously if he or she is able to innovate and try something new.
How Good are Their Programming Skills?
A data scientist needs the skills to not just view and analyze the data, but manipulate it as well. A statistician who reviews and interprets a set of data is very different from a true data scientist who can change the code that collects the data in the first place. A well-rounded programming background, even one that’s not very advanced, greatly increases the candidate’s general capabilities and autonomy.
Are They Problem Solvers?
As the name suggests, data scientists should be scientists that apply the scientific method to data. This means the ability to experiment with data to find models and develop algorithms that have practical applications for marketing and can be used to predict future events. Scientists are inquisitive but follow a strict and rigorous process in their endeavor to find models that are demonstrably useful in the real world.
Is Their Mindset Business-Oriented?
It’s one thing to understand the science and mathematics behind analyzing huge data sets. It’s another thing entirely to truly understand how that data affects profitability, user experience, and employee retention–or any of a myriad other factors important to your business. This trait is especially important in a data scientist executive search. Someone with a background in business will be better at spotting trends that will benefit your business.
Article source: LinkedIn
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