Datanami: Why Data Science Is Still a Top Job

By Brynne Henn - November 18, 2020

Datanami: Why Data Science Is Still a Top Job

Original Article:

For the first time since 2016, data scientist is not the number one job in America, according to Glassdoor. However, Datanami’s Alex Woodie sat down with experts like Sisu CEO Peter Bailis to understand why data scientist will still remain a top job.

“According to Peter Bailis, CEO of Sisu and co-principal investigator of the Stanford DAWN project, the job prospects for would-be data scientists are strong.

“While universities have upleveled and expanded the reach of their data science curricula, the demand for data scientists has also risen,” Bailis says. “I see a huge demand for data scientists and analytics across the board – both at the tech giants and in the broader private sector.”

As technology improves, companies have been able to increase the sophistication of their data operations. Increasingly, that means inserting artificial intelligence (AI) capabilities into the business processes of regular companies (i.e. non-tech giants). And that means demand for data scientists and related positions (research scientists and machine learning engineer) will also go up.

“With better tools for usable machine learning and analytics, the barrier for data science is lower than ever,” Bailis tells Datanami. “Especially with the consolidation of data in standardized formats (e.g., via tools like Fivetran), the time to value for data science investments is lower than ever. The latest platforms for AutoML and augmented analytics bring us much closer to a world where everyone’s an analyst than ever before.”

While the tools are getting better, data scientists looking to accel in the marketplace will still need to have a solid understanding of the basics, including data modeling, relational databases, and basic statistics, Bailis says. Those are critical skills that are likely to survive any future shifts in data science job functions.

And while there will always be new and nifty machine learning techniques, data scientists will usually find better results by focusing their efforts on leveraging the data they already have on hand, Bailis points out.

In the long run, Bailis does see the job of data scientists evolving as the technology changes. As rote tasks that suck up so much time – like data collection, data labeling, model selection, and model interpretation — are automated and the barrier to entry is lowered, the field of data science will flourish, he says.

“AI is letting people focus more and more on what people are best at: understanding the bigger picture from the data, and making creative strategic decisions,” Bailis says. No matter what happens to the data scientist job description, that trend will continue.”

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