As automation grows, data scientists will focus more on business needs, strategic oversight and deep learning and less on model creation and other routine tasks. As automation increases and business needs evolve, so will the ranks of data scientists. But while the future data scientist role may look a little different — with a heavier focus on business operations and oversight — it will be no less important to enterprises.
“As the adoption of automated machine learning platforms … spreads, the role of the data scientist will become less about building models and more about implementing them in a meaningful way,” said Forrester analyst Brandon Purcell. Automation is arguably the biggest disruptor to the data scientist role, according to experts. Automated machine learning vendor platforms like DataRobot, H2O’s Driverless AI, dotData Inc. and Edgeverve already offer a slew of automated and semi-automated capabilities, including feature engineering; model selection and training; extract, transform and load; data preparation; and model deployment and monitoring.
Author: Brian Holak
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