Rethinking AI talent strategy as automated machine learning comes of age

by Brian Cavataio 04/08/2021

Our number one search has been data scientists in our last 5 engagement activities at BWC Executive Group. The reason? Companies are looking to automate their talent strategy.

This article by McKinsey talks about the strategy of machine learning. McKinsey believes it will best serve companies by not putting all its resources into the fight for sparse technical talent. Instead, they focus at least part of their attention on building up their troop of AutoML practitioners, who will become a substantial proportion of the talent pool for the next decade. Developing the models using programming languages such as R and Python by either leveraging one of the many readily available algorithms on open-source platforms or, in much rarer instances, developing a new tailored approach for the problem at hand. Models that require statistical expertise to ensure fairness or build trust—for example, customer engagement models that help salespeople understand what a prospect is likely to buy and why—still require trained data scientists’ expertise. Data scientists with the statistical expertise to understand which tasks can safely be automated without risk will perform highly specialized tasks that can’t automate, such as developing new algorithms or optimizing accuracy down to the last few percentage points.

How to get started? Where should organizations begin to rethink their data-science talent needs? This article by McKinsey gives you an overview of how to go about this approach.

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