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How Freelancing Offers a Solution for the AI and Data Science Talent Shortage

In this special guest feature, Pedro Alves Nogueira, Ph.D., Head of Artificial Intelligence and Data Science and a Director of Engineering at Toptal, observes that due to the low supply of AI professionals, competition to secure available talent is fierce. The hiring of AI specialists and data scientists is primarily monopolized by tech giants like Facebook and Google, which offer exorbitant salaries and competitive perks to AI talent — even those with little previous experience. This puts smaller companies that lack the resources to offer competitive incentives packages at a major disadvantage, and it continues to preclude them from finding talent to develop their technology. Toptal is a global network of the top three percent of on-demand talent in technology, business, and design. In just two years, the company has seen a 145 percent increase in requests for professionals in AI and data science. Pedro is also a professor and researcher on AI at the University of Porto in Portugal, as well as an integrated member of the AI and Computer Science Laboratory (LIACC).

Despite how it may seem, artificial intelligence is not as recent of a development in the technology space as you would think. In fact, the idea of creating artificial neural networks that mirror those of a human brain was first introduced in the 1950s, and the topic has been continuously discussed in academia and private industry ever since.

What is different this time around, however, is the speed with which AI is being introduced into all aspects of work and life. Over the past five years, AI has evolved from a far-off future prediction to a technology that permeates our daily lives in ways that are mostly unnoticed. People interact with AI through their smartphones, laptops, and websites, while companies use AI to automate tasks, scale production, and power decision-making abilities. Coca Cola recently demonstrated a growing reliance on AI in the announcement of its new Cherry Sprite drink – a flavor selected from AI-driven product analysis.

AI has become an crucial focus area for many organizations, with companies being estimated to have invested between $26 – $39 billion in 2016 alone. Even with these already massive investments, the AI revolution is only just beginning. At present, only four percent of companies have actively begun developing or deploying AI, while the remainder are either in the planning stages or have yet to start working with this technology. As the implementation of AI increases, experts predict that investments will reach $15.7 trillion by 2030 — the industry is sure to grow at an unprecedented pace.

Despite the ever-increasing interest and focus on AI, there is a significant barrier to continued growth: the shortage of available AI and data science talent. A recent survey conducted by Ernst & Young found that 56 percent of senior AI professionals identified this issue as the largest barrier to developing new technology. In the United States, where the workforce consists of approximately 150 million people, there are only around 235,000 data scientists. AI professionals are even more scarce at just 300,000 worldwide, with only 10,000 of them qualifying as ‘specialists’. These numbers are staggeringly low when compared with estimates that 58 million AI related jobs will be created in the next four years alone, according to the World Economic Forum’s report, The Future of Jobs.

Due to the low supply of AI professionals, competition to secure available talent is fierce. The hiring of AI specialists and data scientists is primarily monopolized by tech giants like Facebook and Google, which offer exorbitant salaries and competitive perks to AI talent — even those with little previous experience. This puts smaller companies that lack the resources to offer competitive incentives packages at a major disadvantage, and it continues to preclude them from finding talent to develop their technology. As AI becomes more commonplace, tech companies will also have to compete with those in other sectors. Non-tech industries such as media, defense, banking, and automotive are beginning to incorporate AI into their services, further heightening demand from an already shallow talent pool.

I have seen this problem firsthand. Through consultations with clients like Johnson & Johnson, GE, the European Space Agency, and Nike, we have gained an understanding of the obstacles that companies face to secure AI talent. To help meet the demand, there is a need for data science and AI specializations that enable companies of all kinds to access high-quality talent in machine learning, deep learning, data architecture, and data mining, without necessarily having to compete against industry giants. With the new specializations, companies can be empowered to create the innovative and cutting-edge solutions they envision without constraints.

 

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