There is probably no job title loaded with more intimidating terms than “data scientist.” In “data,” you have words like statistics, quantitative analysis, regression, convexity, and standard deviation.
In “science,” you hear vocabulary like experimental groups, controlled variables, false balance, and subject protocols.
When you put them together with “data science,” you get otherworldly jargon, including parallel queries, complex event processing, online transactional processing, and things you might only find in unreadable sci-fi books.
Take comfort, though, data scientists are human like the rest of us.
In fact, the most successful data scientists have skills that most of us pass off as so uneventful that we might not mention them in interviews, put them on resumes or even consider them skills at all.
The minds who put together the data science team behind the edtech behemoth MasterClass revealed to Fast Company that when hiring the people who make their business smart, the valuable skills they considered had nothing to do with an impenetrable lexicon.
Here are the unassuming skills amazing data scientists have.
Range & Adaptability
If you’re only comfortable working within the confines of a database, you might be able to do the technical requirements of a data scientist, but you won’t be able to help a company grow and thrive.
“One of the main assets we look for is a desire to work on projects across a very broad range of analytic disciplines,” says Hristo Gyoshev, one of the team at MasterClass responsible for recruiting data scientists.
In fact, in addition to fluency in Excel, SQL, Python and other languages essential to data science, Gyoshev and MasterClass prioritize candidates who come in with unique and varied experiences across different industries & sectors.
The ability to consider the big picture
One of the biggest differences between a data scientist and a data analyst is that while they are both tasked with using structured & unstructured information to help a department, a data scientist understands how that request works in the context of broader company strategy.
“Strive to understand and keep in mind the broader context of the problem you are being asked to solve—or the problem behind the question you are being asked,” Gyoshev suggests.
If you pride yourself on being able to strategize the micro to help push the macro, you might want to consider data science.
Problem-Solving
Data science is on the frontier of business growth. Ask anyone who has pushed those frontiers into the unknown and you are, by definition, going to encounter problems.
It’s how someone handles unforeseen problems that determines if they will be an adequate or exceptional data scientist.
Because of this, MasterClass puts a heavy emphasis on how well candidates have dealt with solving problems in their professional career. “We may ask for examples of specific types of projects they have worked on, and then ask them to walk us through their approach and thinking, the tools they used, the major challenges they encountered, and how they resolved them.”
Thinking outside the box
Much like being comfortable dealing with unforeseen problems, data scientists shaping the frontiers of business need to be comfortable working outside of traditional data wrangling. For MasterClass, this means “building various predictive models; designing, conducting, and analyzing surveys or experiments; helping to define and set up reporting and metrics; and conducting one-off analyses related to various aspects of our business operations.”
The drive for data
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