Every day tech industry execs bemoan the lack of data scientiststhe people who theoretically know how to look at the data your company generates, and delve into it to derive the all-important insights we keep hearing about. Several actually said the time to hire a data scientist was: yesterday. What do you expect? Its good to have the data person in place before you start mapping out plans for what youre going to do with the data and whether and how to clean it up.
But hiring a data scientist presupposes that there is a data scientist out there to hire and therein lies a problem. According to Dr. Tara Sinclair, Indeed.com’s chief economist, the number of job postings for data scientist grew 57% for the first quarter this year compared to the year-ago quarter. And searches for data scientist grew 73.5% for the same period.
One problem with job post data is that “data scientist” is a loosey-goosey term. Generally speaking, practitioners are expected to know statistical analysis, predictive modeling and programming. Oh, and having a certain artistic flair to guide how results are visualized is a definite plus. But ask a dozen hiring managers and you may get a dozen variations on that theme.
Another note: While technical expertise is important, there may be more important attributes. “We look for raw inquisitiveness, the intellectual curiosity which will repay you ten fold. They’ll be so annoying about the data sets they want and the introductions they need that they’ll drive you crazy,” he said.
Existing tools like Tableau have already sweated much of the complexity out of the once-very-hard task of data visualization, said Raghuram. And there are more higher-level tools on the way from a cadre of second-generation data science companies that will improve workflow and automate how data interpretations are presented. “That’s the sort of automation that eliminates the need for data scientists to a large degree,” he said.