Today’s data science roles won’t exist in 10 years

In the coming decade, the data scientist role as we know it will look very different than it does today. But don’t worry, no one is predicting lost jobs, just changed jobs.

Data scientists will be fine — according to the Bureau of Labor Statistics, the role is still projected to grow at a higher than average clip through 2029. But advancements in technology will be the impetus for a huge shift in a data scientist’s responsibilities and in the way businesses approach analytics as a whole. And AutoML tools, which help automate the machine learning pipeline from raw data to a usable model, will lead this revolution.

In 10 years, data scientists will have entirely different sets of skills and tools, but their function will remain the same: to serve as confident and competent technology guides that can make sense of complex data to solve business problems.

AutoML democratizes data science

Until recently, machine learning algorithms and processes were almost exclusively the domain of more traditional data science roles—those with formal education and advanced degrees, or working for large technology corporations. Data scientists have played an invaluable role in every part of the machine learning development spectrum. But in time, their role will become more collaborative and strategic. With tools like AutoML to automate some of their more academic skills, data scientists can focus on guiding organizations toward solutions to business problems via data.

In many ways, this is because AutoML democratizes the effort of putting machine learning into practice. Vendors from startups to cloud hyperscalers have launched solutions easy enough for developers to use and experiment on without a large educational or experiential barrier to entry. Similarly, some AutoML applications are intuitive and simple enough that non-technical workers can try their hands at creating solutions to problems in their own departments—creating a “citizen data scientist” of sorts within organizations.

In order to explore the possibilities these types of tools unlock for both developers and data scientists, we first have to understand the current state of data science as it relates to machine learning development. It’s easiest to understand when placed on a maturity scale.

Copyright © 2020 IDG Communications, Inc.

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