Typically, job specifications for data scientists list a graduate or post-graduate degree as a key requirement. Yet two of the most acclaimed data scientists—Nate Silver and former Billy Beane/Moneyball assistant Paul DePodesta—each lack a Ph.D., according to a recent Forbes article on the topic.
In most cases, prospective employers look for candidates who have a strong background in statistics, data mining, computer science, and/or algorithmic skills. The Forbes article enunciated that data scientists [require interwoven skills]: “Data science is a combination of two distinct skill sets. One is the traditional “tech-centric” skill set that helps curate, cleanse, secure, and contextualize the Big Data. The other skill set is the “pure analytics” that bring business domain knowledge and a data mining mindset.”
CIOs and other senior executives seek individuals with solid quantitative skills, including the ability to find patterns or anomalies in Big Data sets—for instance, the capability for a data scientist to comb through point-of-sale (POS) data and analyze the transactional activity associated with a retail clerk who may be suspected of fraud. In other cases, a data scientist may scour data to help identify the factors responsible for downturn in sales for a specific product, category, region, or customer segment.
Multiple research studies indicate that the existing Ph.D.-level talent pool is insufficient to fill the industry’s demands for data scientists. In fact, in a recent Accenture report almost 80% of the data scientist positions that were created between 2010 and 2011 have still not been filled.
Ultimately, business leaders want data scientists who can help them find answers to problems that are stumping them—identify trends or patterns that are not necessarily evident. For instance, a data scientist can help a consumer packaged goods (CPG) company identify how a competitor’s recent price reduction has cut into their own sales and profit margins. A data scientist can detect a recent downturn in customer satisfaction or an increase in Telco customer churn, and then figure out the root cause behind the issues. In addition, good communication skills—the ability to tell a story with data and influence decision-makers with hard facts, although elusive, is a must-have for a data scientist to have a positive impact on the business outcome.
A data scientist, with the right blend of quant or statistical skills and technical orientation, complemented by strong business acumen can be priceless for an organization.
