4 qualities to look for in a data scientist

Jonathan Hassell

It's hard to resist the sparkly nirvana that big data, leveraged appropriately, promises to those who choose to embrace it. You can transform your business, become more relevant to your customers, increase your profits and target efficiencies in your market all by simply taking a look at the data you probably already have in your possession but have been ignoring due to a lack of qualified talent to glean value from it.

Enter the data scientist — arguably one of the hottest jobs on the market. The perfect candidate is a numbers whiz and savant at office politics who plays statistical computing languages like a skilled pianist. But it can be hard to translate that ideal into an actionable job description and screening criteria.

This article explains several virtues to look for when identifying suitable candidates for an open data scientist position on your team. It also notes some market dynamics when it comes to establishing compensation packages for data scientists.

Because "data scientist" represents a bit of a new concept, without a lot of proven job descriptions, you'll want to work closely with your human resources department on the rubric and qualifications you use to screen initial resumes and also set up a first round of interviews. What follows are five salient points that should prove useful as qualify candidates for a data scientist role.

1. A Good Data Scientist Understands Statistics and Laws of Large Numbers
Trends are seen in numbers. For example, a good data scientist understands, "This many customers behave in this certain way" or "This many customers intersect with others at this many precise points." Over large quantities of data, trends pop out in numbers.

A great data scientist has the skillset to understand trends in large numbers and an ability to translate that into predictive analytics. A good data scientist can interrogate large quantities of data and extract trends, then use predictive modeling techniques to anticipate behavior across that aggregate dataset. Statistics are also helpful in preparing reports for management and prescribing recommended courses of action.

While a mathematics degree would be ideal, many qualified candidates have taken a slightly more practical academic path. Don't be scared away by interviewees who lack advanced mathematics credentials. A focus on statistics in a candidate's academic career, whether at the bachelor level or above, would prove sufficient for this type of position.

2. A Good Data Scientist Is Inquisitive
Part of the allure and mystique of big data is the art of teasing actionable conclusions from a giant haystack of (typically) unstructured data. It's generally not enough to know how to write queries to find specific information without being able to generate the context of what queries should be run, what data we would like to know and what data we might not know we would like to know but that could possibly be of interest.

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