When I started my career in data 15 years ago, I could never have envisioned a sexy rebranding of my work with the coining of the term “data scientist,” let alone the immense popularity it’s achieved in recent years. Widely considered one of the worlds hottest and most sought after positions, data scientists are re-writing what it means to be cool in the modern tech era.

There has never been a better time for my fellow nerds. Jobs are overflowing with demand far exceeding supply.

The industry has become so hot it’s not uncommon for board members of startups to demand hiring of data scientists early in the product life cycle. It is in that capacity that I’m frequently brought in to meet with executives and more often than not, inform them that they do not need a data scientist.

When new prospective clients come to me, at least 50 percent of the time it’s under the guise of “My CEO/board member/etc. told me I need to hire a data scientist.” To which I generally ask the following four questions: Related: 5 Things to Keep in Mind When Using Data for Artificial Intelligence There are many techniques that use less data than deep learning, however, they still require reasonably large samples, not to mention a working knowledge of when to use which methodology.

There is still valuable work to be done at this stage to create an environment where data science can thrive in the future, it just doesn’t require a full-time, expensive resource to achieve. Without basic understanding of what drives the organization, it’s going to be very difficult to make use of advanced techniques. For example, a data scientist can use machine learning to make predictions such as which users will churn or become highly active, however, if the business does not have a definition for churn or highly active, that becomes a requirement prior to building the predictive models. Read more from entrepreneur.com…

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