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The Death of the Data Scientist

MMS Founder

Article originally posted on Data Science Central. Visit Data Science Central

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

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