Alariss Insights

AI Talent: 3 Ways To Get Ahead Of The Next Hiring Wave

July 17, 2019

Recent headlines suggest a lack of qualified talent for the exploding artificial intelligence and machine learning industry. 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 talent, 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.

Dell AI technology

What is AI Talent?

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”.

3 Ways To Get Ahead

  1. Forget the Buzzwords: AI/ML 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.
  2. Move Past the Technical: 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.
  3. Will They Understand Your Business Goals: 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.

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