No Luck Recruiting AI Talent? You’re not Doing it Right

Unless you can compete on salary with the likes Google and Facebook, fill out your artificial intelligence and machine learning teams using alternative approaches.

Guest Commentary, Guest Commentary

December 18, 2017

5 Min Read

Recent headlines like “Artificial intelligence sector needs more brain power” frantically suggest a dearth of qualified talent for the exploding artificial intelligence and machine learning jobs sector. In fact, research shows there are fewer than 10,000 individuals globally with the skills needed to successfully lead an artificial intelligence or machine learning project, leading to intense competition and sky-high starting salaries for available talent.

Clearly, AI-related positions are hard to fill, but companies don’t have to play in the high-intensity pool that competes for a rarified group of candidates. What’s the alternative you ask? Stop looking for the wrong skills and at the wrong group of candidates. In a field as fast-moving as AI, hunting for the perfect candidate is a recipe for disaster. Instead, companies seeking AI/ML talent need to reframe their definition of “qualified” and expand their perspectives in order to find the talent that’s hiding in plain sight. 

Reviewing resumes? Don’t play Buzzword Bingo

Granted, if you’re hiring an AI/ML engineer, there are some basic requirements: they need a solid grasp of computer science fundamentals and experience applying them. Some college-level understanding of probability and statistics is certainly necessary. Typically, recruiters also look for “buzzy” phrases that might be found on a “qualified” candidate’s resume, like “data modeling”, “anomaly detection” and “application of neural networks”. 

However, candidates are increasingly sophisticated in their self-marketing and are well-versed in playing “buzzword bingo”. Participating in that game can lead a recruiter to ruin. By using buzzwords to sort through resumes and narrow a search down to the perfect candidate, recruiters may find there’s actually no substance beneath the veneer of carefully chosen phrases and manicured presentations.

Recruiters can find outstanding candidates by looking beyond the skill sets typically listed in an AI/ML job description. What makes an excellent AI/ML engineer is not simply technical acumen that maps to the buzzwords du jour or the ability to check the correct boxes on a resume. An exceptional AI/ML engineer not only has the right technical chops for the job, but also the capacity to consider problems holistically and the ability to integrate ideas from different disciplines to identify and solve cutting edge problems.

An excellent AI/ML engineer understands both the larger business problem and the entire technical ecosystem that they’re designing for, not simply the algorithm needed to identify the anomalies and outliers in a data set. They also have the ability to write for that entire system and have it work. Since, arguably, no other technology sits squarely at the intersection of human behavior, decision making, and technology, it is crucial that AI engineers understand all the systemic interrelationships that make up a problem. If you hire someone without that ability, you may find yourself with an AI engineer who can write working software, but will never successfully integrate all the technical and business elements needed to arrive at a solution that addresses the problem holistically.

Alternative backgrounds make for awesome AI/ML talent

Another way to expand your pool of “qualified” candidates is to look for individuals with nontraditional backgrounds. When companies are looking to hire talent like operations or salespeople, industry-specific experience is a definite benefit. While competition for the best candidate in these fields is still intense, the arenas are well-established enough to offer a more robust pool of suitable candidates, so the challenge is more manageable. 

But when it comes to a field as new as ML and AI, limiting your search to individuals with years of experience will quickly whittle the applicant pool down to next to nothing. Unless you’re ready to shell out $500k as a starting salary, good luck trying to outcompete the Facebooks and Googles of the world. 

Instead, recruiters should seek out people who have crossed over industries, changed roles, or have, through a diversity of experiences, demonstrated that they are connectors and integrators. These candidates offer the adaptive thinking necessary to build the future of artificial intelligence as it unfurls. And, most people will completely overlook them. 

Lead with vision, not salary

Once you’ve begun building your pipeline of candidates, you’ll have to sell them on why your company is the right place for them to sharpen their skills and build their career in AI and ML. 

By sell, I don’t mean throw a flashy salary at them. 

Sure, you’ll have to make your offer competitive with other offers, but AI talent is like any other talent. If the only reason a candidate wants to work for your company is the impact on their bank account, they’ll quickly jump ship the second a nicer paycheck comes along. 

Instead, lead with your company’s vision for turning AI into real-world impact. Whether that impact is improving diagnoses for healthcare, changing the way companies use data, or making the roads safer for all of us, many who are getting into AI are doing so because of the tangible impact they can have on the world around them. If you can get AI and ML talent that buys into your company’s vision, and you can clearly demonstrate how they’ll have a role bringing that vision to life, you’ll be surprised how much more successful you are in your hiring efforts. 

In order to stay ahead in artificial intelligence hiring, organizations must reexamine their assumptions and transform their approaches if they wish to control their own destiny. The winners in the competition for AI/ML talent will be the organizations that, like the candidates they seek, make the connections required to develop a new perspective on an old problem. 

Amit Prakash is co-founder and CTO at ThoughtSpot and has deep experience in building large-scale analytics systems. Prior to ThoughtSpot, Amit led analytics engineering teams in the Google AdSense businesses. Prior to that, Amit was a founding engineering in the Bing team at Microsoft, where he implemented the pagerank algorithms for search from scratch. Amit received his PhD in Computer Engineering from the University of Texas at Austin and a Bachelor of Technology in Electrical Engineering from the Indian Institute of Technology, Kanpur. 

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