Editor’s note: This article was originally reported and published in April 2021. It was updated with new information in July 2022.
Data scientists are one of the most sought-after roles in corporate America today, because organizations, armed with the right talent, can drive more value from its data.
However, data scientist roles are evolving as a matter of technological innovation and market maturity. In fact, the titles of statistician, actuary and quant, depending on the industry, preceded the title of data scientist.
There are some challenges when it comes to determining how the data scientist role is changing, however. For one, despite the high demand for data scientists there aren’t clear requirements for the job.
What is data science?
Data science, as defined by today’s industry professionals, is the study and use of data to inform business decisions and create new customer-facing products. Data scientists are typically responsible for data analytics to find new insights. They often work with advanced machine learning models to predict future customer or market behavior based on past trends.
The ultimate goal of what businesses hope to get from data scientists isn’t expected to change. But how data scientists accomplish those goals is likely to undergo substantial alterations in the years ahead.
Does data science have a future?
Experts have said that 80% or more of a data scientist’s job is getting data ready for analysis. Now, technology providers sell platforms that automate tasks and abstract data into low-code or no-code environments, potentially eliminating much of the work currently done by data scientists.
“[The data scientist title] will probably fade into the background because more tools are becoming prevalent,” said Kathleen Featheringham, director of AI strategy and training at management and IT technology consulting firm Booz Allen Hamilton. “To me, it’s like website design years ago when you had to have people who really like code, but now you can go online and use a tool that will build your website for you.”
[The data scientist title] will probably fade into the background.
Kathleen FeatheringhamDirector of AI strategy and training, Booz Allen Hamilton
Will AI and automation replace data scientists?
Predicting the future of artificial intelligence requires understanding its past. The earliest realm of data science — analytics or stochastics — incorporated probability theory and analysis into programming. The R language emerged as an open source equivalent of SASS and SRS, two ancient analytics packages that trace their lineage back to Fortran. Python’s incorporation of similar packages made it the go-to language for combining the results of such data analysis with other components.
These gave way to visual pipeline tools such as Alteryx or Microsoft BI, which reduced the need for programming experience, yet required enough understanding of statistics to know what these packages were doing. It is unlikely that the need for competency in modeling such pipelines will ever fully go away, so while the notion of being a dedicated data scientist will fade, the need for subject-matter-expert analysts will continue.
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Source: https://www.techtarget.com/searchenterpriseai/feature/The-future-of-data-science-jobs