You’re in the market for a stand-out data scientist. The first challenge to hurdle is that everyone else is also looking for a superstar analyst. The second no-less-challenging obstacle presents as a job description that may be difficult to pinpoint, coupled with the reality that industry standards and benchmarks are less than exact. That’s two perplexing scenarios, isn’t it?
When it comes to finding the right data scientist for your organization, there’s no shortage of opinions on what qualities, skills, and traits should top your analyst wish list.
According to technology and business writer Bob Violino, risk analysis, process improvement, and systems engineering each rise to the top of the list. “A sharp data scientist needs to understand the concepts of analyzing business risk, making improvements in processes, and how systems engineering works.”
Celeste Fralick, a chief data scientist at security software company McAfee, concurs. “I’ve never known an excellent data scientist without these skills. They all play hand-in-hand, both inwardly focused to the data scientist but outwardly to the customer . . . as these are all skills that data scientists require to probe the customer about what problem they are trying to solve.”
More than just a science . . .
Ariella Brown notes that Venture Beat once suggested that “data artist” may be a more accurate job title. “Perhaps these scientists are not the Einsteins and Edisons but the Van Goghs and Picassos of the big data revolution.”
The take-away? That this science goes beyond merely observing and quantifying data. It regularly requires creative approaches to extract insights and value from said data.
“A successful data scientist is not just someone who has checked off the list of hard skills,” notes Brown. “He or she has to have the ability to think about how to approach a problem in a new way that opens the way to a solution and then effectively communicate what worked and why. Far more than a mere quant, the successful data scientist is a creative thinker and problem solver with domain understanding.”
Soft skills that factor into success in data science include—
- Attention to detail
“In many ways, a data analyst’s job is similar to searching for a needle in a haystack,” states Ashley Brooks. “Data analysts must be able to notice the small clues that point toward a larger message that’s hiding in a group of data. Attention to detail also comes in handy when data analysts build the processes that efficiently capture and sort data. A small error in a single line of code can throw the entire workflow awry. Data analysts should always be on the lookout for tiny mistakes that can lead to larger problems in the system.”
· A dubious nature
Micah Pratt suggests, “A good data analyst must understand their work does not reveal perfect answers in any circumstances. For example, if they are tasked with determining the response rate of a marketing promotion that offered a skip-a-payment on an auto loan, the analyst must figure not all customers who skipped a payment that month were responders to the campaign. Some may have forgotten to mail a payment or may have started to default on the loan. The analyst understands how to adjust the analysis to reveal the best data.”
- Teamwork
While an analyst may carry out his/her work in a solitary setting, data analysts must collaborate with co-workers throughout the process. From the web developers to fellow analysts to company leadership, using a team approach is essential to glean the most insight from the various data sources and chart a course based on those insights.
In Every Data Science Interview Boiled Down To Five Basic Questions, Roger Huang presents questions that seek to test this vital mix of skills and aptitudes. Of those five questions, 60% address hard skills, 20% access soft skills, and the remaining 20% gauges the ability to apply knowledge to a situation.
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