The AI Skills Gap

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Dialpad’s chief strategy officer, Dan O’Connell, says his ideal AI/ML team is built with two key groups. “Ph.D.’s or people with academic research experience can really motivate others to push the boundaries of innovation. Engineers are also important for their hands-on experience building real-world products in a business environment and expertise in text analysis and data mining,” says O’Connell.

Pros who can manage content and business applications related to AI are also highly valued. “Say you have a great machine learning algorithm and dataset. How do you apply it in the real world? Right now I’m very focused on building custom voice assistants, but I see a huge gap when it comes to authoring those kinds of applications and creating real-life conversations and the kind of dynamic, nonlinear storytelling that comes naturally to humans,” says Majer. “There’s very little talent of this sort to be had out there.”

As in any industry, experience matters.

“The basics are needed, of course, but workplaces particularly value skilled specialists who can parse data,” says Ravid.

Cao believes the AI/ML skills most prized by employers today include these:

  • Programming
  • Machine learning
  • Statistics, probability
  • Algorithms
  • Data analytics
  • Visualization
  • Distributed computing
  • Signal processing
  • Cognitive science
  • Problem solving

Praveen Mandal, CEO and cofounder of 2predict, says the recipe for the ideal staffer is someone who possesses deep programming skills, computer science skills, and mathematics skills combined with domain expertise.

These smarts are needed for all modes of AI/ML heavy lifting, “including preparing datasets, developing algorithms, developing production-grade models, training models, and debugging models,” says Mandal.

Least in demand are “coders writing Python and using basic algorithm tools,” notes Spera.

Soft skills like team-building abilities and a friendly, social personality shouldn’t be overlooked, either. “Strong analytical skills, good intuition for statistical significance, and ability to deliver creative solutions are important qualities in such employees,” says Livne.

“For me, pragmatism is another key skill,” Spera says, “because data science problem and solutions are very complex. Someone who can translate the problem into layman’s terms to highlight the business value of what they produce is a big win.”

Wield at least some of these rare competencies and you could command big bucks. A New York Times article reported that top AI professionals can make $1 million or more; even AI specialists with little to no workplace experience often earn between $300,000 and $500,000 annually in salary and stock. The top employers snatching up most of these pros include Microsoft (with nearly 15,000 AI-skilled employees), IBM (over 11,000 employees), Google (more than 10,000), Tata (6,348), and Amazon (6,217).

Attracting and Keeping AI Stars

Want to compete with these big boys and other AI talent raiders? Prepare to roll out the proverbial red carpet for even fresh graduates and moderately skilled prospects.

That means offering attractive employee compensation and benefits packages, training and professional development opportunities, and a flexible schedule conducive to better work-life balance, Cao advises.

“Highly technical talent want three things,” says Mandal. “They want to work on really interesting and stimulating projects, to work for a good mission-aligned culture, and to be compensated well.”

If you want to lure better candidates to your enterprise, work on building up a roster of AI/ML talent who can serve as an effective recruiting tool.

“Be intentional about highlighting the great work of your existing team,” says O’Connell. “The interview process is a great time to do this; you can introduce candidates to the different backgrounds, skills, and stories of current team members as well as all the exciting AI projects yet to come. Not only will this encourage top talent to join, it will make current team members feel valued and appreciated, too.”

A related point: Remember that turnover in the AI/ML field can be high. So work on improving retention of your skilled employees and making them happy.

“Just because an organization can hire experts does not mean they know how to lead or manage them. Put a proper manager in place who can effectively lead the research-oriented approach to extracting value from your data,” suggests Mandal. 

Erik J. Martin is a Chicago area-based freelance writer and public relations expert whose articles have been featured in EContent Magazine, Reader’s Digest, The Chicago Tribune, Los Angeles Times, and other publications. He often writes on topics related to technology, real estate, business and retailing, healthcare, insurance, and entertainment.

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