Recently, McKinsey & Company issued a report entitled “Using marketing analytics to drive superior growth.” Marketers already know how the development of more advanced analytical tools in recent years- most notably artificial intelligence and machine learning- has given them a big boost in their decision-making firepower. But this particular report had a talent angle to it, which of course caught my attention as a data science recruiter. That angle examined the “art versus science” aspect to data-driven decision making and key data science skills.
In a recent blog post on data science, I wrote that in the struggle between art and science, science prevails. After all, at its core, analytics and data science is about measuring, managing and analyzing. But the McKinsey piece is noteworthy because it talks about the “softer” data science skills that are needed to make better business decisions, and based on my experience, that is an area that employers tend to under-emphasize when they’re interviewing these candidates. The need for softer data science skills is a concept I am intimately familiar with. As a recruiter who has placed marketing analysts for more than 25 years, I definitely recognize a good softer skill when I see it. Softer skills have always been a highly sought after attribute in marketing analytics- that’s nothing new. But the argument can be made that in the age of digital data, employers need these softer data science skills more than ever. Businesses that market online are swimming in data, and much of it is in unstructured form- tweets, blogs, posts, you name it. That represents potentially a huge asset for businesses that want to make smarter decisions, but as any digital marketer knows, the quality and availability of that data can be absolutely all over the map. How do you access it, and most importantly, how do you leverage it to benefit the business? That’s the kind of creative thinking employers are looking for these days, and it is one of the defining characteristics that separate the “green shade” number crunchers from the ones who can help generate meaningful insights that business leaders can act on.
I’ll leave it to the experts at McKinsey and other research firms and think tanks to discuss the best analytical approaches, how to build hadoop solutions, etc. But from a data science recruiter perspective, I have plenty of experience to draw upon when it comes to marketing analytics. Want to impress your next interviewer? Don’t spend too much time emphasizing your statistical prowess. Show them how your work has made better business through better targeting, better forecasting, better pricing, better reporting, better decision-making- in other words, using those softer data science skills. THAT’s how you get invited back for that second interview!
