In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot racers, and human alarm clocks.

In 2000-2010 data science was dominated by masters of herculean subjects, with PhDs in linear algebra and statistics, combined with expertise in the uncelebrated (at the time) field of coding.  Data science truly had an emphasis on the science of manipulating data, focusing on how to mathematically validate significance and trends.  This was a great first step in helping society gain insights from the massive influx of big data, however it now has its drawbacks.

Tipping the balance too far towards degrees of freedom and vectors is great in the ivory towers of academics, but when it comes to practical and timely results for businesses, is not ideal.  I recently heard a story about a team of PhD data scientists at a Fortune 500 company having trouble improving their built from scratch multi-layered neural network model’s accuracy. They spent hours meticulously tuning cryptic hyper-parameters and adding layers to their model with no success.  The data then ended up falling into the hands of an employee fresh out of his undergraduate degree. After quickly looking at the data, his first step was to create a simple regression model and remove all zero values, immediately skyrocketing accuracy, and creating a cluster of self-conscious PhDs.  Despite his lack of experience with scalar multiplication or multi-threading programming, his domain and practical knowledge made all of the difference.

With the increasing power of user-friendly tools and GUIs, and a data science course seemingly available on everywebsite, being able to perform data science will eventually be like being competent in Excel.  Just knowing the ins and outs of data science as a skill will not be enough. The tools will be powerful enough to handle the data “sciencey” aspects, and the fundamental concepts will be taught throughout school, evolving data science into a skill integral to every job role, not a title.  There will be no more data scientist roles, just roles that use data science.

For now, before the data scientist role goes into retirement, these forces of user-friendly tools and democratization of knowledge is increasing the potential of beginner data scientists to get powerful results with the right training.  Beginner data scientists are spearheading advanced AI across Fortune 500 companies developing deep learning computer vision and natural language processing models for predictive maintenance of assets, facial recognition, and generating valuable insights from social media and news.  Data science managers should be raising their expectations of what their teams can achieve, and be willing to invest in training their teams to get them confident with advanced techniques.

Ultimately, although the role of data scientist may be in its golden years, it still currently has amazing opportunities to create transformational changes across businesses, and should be leading the odds to fourpeat Fast Company’s best job award in 2019.

Learn how to get your team of data scientists at any level jump-started to computer vision and natural language processing at https://www.textvox.ai/

Views: 64056


You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by Domingo Cordero on July 13, 2018 at 7:51am

"The tools will be powerful enough to handle the data “science” aspects, and the fundamental concepts will be taught throughout school, evolving data science into a skill integral to every job role, not a title."

I frequently discuss if we need and specialized data science group or we can integrate data science skill to each department analyst role(like HR, Accounting, Capital Projects .....)

The critical part of this discussion, if the expert insight as a valuable strategic asset, this inclines me to think  more in the mentioned approach  "integrates into every job role, not a title." 


Comment by Mike Richmond on July 13, 2018 at 7:39am

I have come to learn that Data Science is as much art as it is science.  Most of the science can be automated and reduced to a button, and there is no lack of novices willing to press the button to get an answer.  But the moment that something goes wrong, or some assumptions aren't met, the novices either are ignorantly producing bad results or unable to make the adjustments necessary.

The anecdotal example in Matt's blog is adorable, but to assume data science is dying just because a "team of PhD data scientists" forgot about the principle of parsimony is quite a leap.  We have entered a new era where we all have the entire world of knowledge in our pocket, and it is available at lightning speed.  One of the drawbacks about this ability is that we are slowly forgetting how to learn.  Being able to spew out facts is not the same thing as interpreting and tweaking results, and business acumen plays a big role in those exercises.  You simply cannot learn business acumen in school or one of the "courses seemingly available of every website", and "being able to perform data science will eventually be like being competent in Excel" is (in my opinion) a false equivalency.

I am reminded of scenes from two movies by this discussion: 

The first is the original Jurassic Park, where Jeff Goldblum's character has just observed dinosaurs for the first time, and learned how they 'made' them.  He says something like "You are standing on the shoulders of giants, and never stopped to think if you should, you just did it". 

The second is from Willie Wonka and the Chocolate Factory (the Johnny Depp version), where Charlie's dad loses his job of screwing on the cap for tubes of toothpaste because it becomes automated, only to return to the factory as the guy who fixes the machines that do the job he used to do.

Many years ago, the death of the computer programmer was predicted, because the smart money said creating programs was a task that should be easily automated.  Didn't happen...

One of my favorite philosophies in life is this:  The value of experience is rarely understood or appreciated by those who don't have it.

Comment by Ralph Winters on July 13, 2018 at 7:07am

Not sure I understand your example about the failings of a neural network vs. the "workable" linear regression solution.  Why invest your time and money on complex algorithms that are impossible to understand and maintain, when putting the effort into really understanding linear regression ,which IS about knowing your domain (and knowing about degrees of freedom by the way!)  can be more valuable.  I think that is what you are saying.

Comment by Matt Tucker on July 13, 2018 at 1:58am

Hi Alex,

I'm interested in hearing your opinion, care to elaborate?

Comment by Alex Constantin on July 12, 2018 at 9:21pm

I’m afraid that the holes in this argument fittingly resemble those in DS / ML solutions made by non-Data Scientists.

© 2021   TechTarget, Inc.   Powered by

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