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8 months ago
How Can We Fix the Data Science Talent Shortage?

 
Originally published in Springboard Blog, Jan 22, 2021.

Data science might just be the most buzzed-about job in tech right now, but its pop culture sheen conceals some of the harsh realities of being a fresh graduate in the industry.

The job topped LinkedIn’s yearly Emerging Jobs Report from 2016 to 2019 consecutively (it is now at #3). But when Springboard data science alum Kristen Colley started hunting for her first data science job in 2019, most companies were not interested in her data science credentials. “When I started rebranding myself as a data analyst with the ability to handle machine learning problems, that’s when the opportunities started coming in,” she said.

Colley’s experience is part of an emerging trend in the way companies hire data scientists. With the mainstreaming of automated machine learning (autoML) and DataRobot, an AI platform which can train and tune machine learning models, businesses don’t necessarily need full-fledged data scientists who can perform end-to-end data processing, from exploratory data analysis to building ETL pipelines—at least not for junior roles.

“If you want that high-paying data science job you signed up for, you’re going to have to wait a few years,” said Hobson Lane, a Springboard data science mentor and co-founder of Tangible AI. “They’re moving up the skill level chain because they can now get much of what they need for data science from DataRobot and autoML.”

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