How Data Scientists Are Wasting Their Time Posted on : Feb 15 - 2018

Today’s definition of what most companies want in a data scientist seems to be something akin to a superhero. Companies are looking for a regular Mister Fantastic (the Marvel Comics’ superhero who was “one of the bravest adventurers and most brilliant scientific minds of his generation”).

Chief A.I. Officer at ZIFF, Ben Taylor, has coined the ideal caliber of data scientist as a ‘Type-E’ – a kind of over-achieving, unapologetically ambitious, narcissistic, nerd-on-A.C.I.D, who eats, drinks, sleeps, and is married to data science.

However, if we want to mold this perfect data scientist then we first need to identify and eradicate their imperfections. So, let’s take a quick look at ways in which data scientists fail:

Data Scientists are Primed to be Researchers not Business People

Taylor sprints past Malcolm Gladwell’s theory that it takes 10,000 hours of experience to make an expert, and claims you need at least triple that to make a semi-decent data scientist. This means that data scientists need to start gathering practical, real experience very early in life.

Data Scientists are generally highly qualified academically. Research shows that 88% of data scientists hold a minimum of a Master’s degree, 46% hold a Ph.D., while only 1% have no third-level education.

While academic qualifications are certainly fundamental, colleges place too much emphasis on coursework and academic research and not enough on training data scientists for the world of business. This results in the formation of data scientists who lack the vital business acumen needed to be able to prioritize projects and to choose those that are aligned with business objectives rather than those that they think would be exciting and cool.

Data Scientists Over-complicate Things

Data scientists often fall into the trap of over-complicating projects because they know that they have the brains and skills to do so. Like any scientist, data scientists are at risk of being too overly concerned with breaking ground, doing what’s never been done before, outsmarting their peers, and creating the most mind-boggling algorithms and mind-bending visuals that they lose sight of the main goal of any business: profit or business value.

Instead of focusing on how complex and impressive they can make their code, data scientists need to focus on how to achieve the highest return for the least amount of time and money. They should start simple and then build upon this infrastructure as the project proves itself worthy of the required time and effort. Data scientists should also know when to kill a project as soon as it shows signs of being irrelevant, something the tenacious data scientist is bad at. View More