Subscribe to DSC Newsletter

Interviewing data scientist candidates? Ask these questions | TechTarget

I would also add: Tell me about three of your success stories, and how was success / lift measured?

Here's the article:

Bill Franks has a love-hate relationship with the term "data science."

"It's way over-hyped," said Franks, author of the recently released Taming the Big Data Title Wave and chief analytics officer for the data warehouse appliance vendor Teradata. "If you look at what people described as data scientists are doing today, they're doing what I've always done and what I've always looked for from great analytic professionals."

While they may be using a new set of tools on new types of data, such as social, an analytic professional's job description hasn't changed all that much: thought process, analytic goals, deriving value for the business, Franks said, it's all the same thing.

But he also embraces the new title as a label for the analytics professional he tends to seek out: Someone who has an analytical technical background as well as commitment, creativity, intuition, business savvy and presentation skills -- what Franks refers to as "softer skills." recently sat down with Franks to discuss why these non-traditional skills are necessary, how businesses can assess a candidate's "softer skills" during the interview process, and why he's begun calling for data artists rather than data scientists.

You recently started using the term "data artist." Why?

Bill Franks: I don't expect people to literally adopt that term as opposed to data scientist. It's more of me trying to provoke thought about what really makes a good data scientist. An analytic professional could be a data scientist, data modeler or data miner. And the conclusion I've come to is that the technical skills required for the job are important, but they're not what differentiates super successful analytical people from the run-of-the mill or the not-so-successful. Some of the traits are things that are often not specifically associated with hardcore analytics people.

Be more specific: How does something like creativity impact the analytics process?

Franks: As much as you'd like to follow the book and go by what the formulas say, the data is never as complete as you'd like, the data is never as clean as you'd like, and the problem is never as well-defined as it would be in a textbook. The really good analytics professionals and data scientists are those that are able to understand the business problem; they're able to apply creativity and present the results well.

How does an analytics professional pick up these "softer skills?"

Franks: It's sort of like athleticism. There are people who are athletic and those who are not. And those who are inherently athletic, you can put them on a basketball court or a soccer ...

Read full interview at

Views: 2241


You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

On Data Science Central

© 2021   TechTarget, Inc.   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service