Machine Learning: Finding the Right Candidate for the Job

Machine Learning: Finding the Right Candidate for the Job

Artificial intelligence has been dominating headlines for years, with doomsday cries about robots stealing jobs and Jetsons-esque predictions about space travel. But underneath all of that hype, machine learning has made huge advancements, and companies are hungry to reap the benefits, whether it be to power a new cybersecurity tool, create customized shopping experiences, or power better search capabilities.

Of course, the first step for any company that wants to actually build or implement machine learning applications is finding the right talent. Data shows that machine learning is one of the most sought-after skills in tech. Combine that with the short supply of qualified candidates, and it’s easy to understand why salaries in data science and other fields are steadily increasing. Yet, many companies are still struggling to wrap their head around how they can find the right candidate for the job.

Hiring machine learning talent is challenging for several reasons. Not only is there a short supply of talent, but it’s also challenging for hiring managers to identify the correct candidate for the role. Because the many of the required skills aren’t something that can be learned from a textbook, candidates will not only need to apply the algorithms they’re familiar with, they will need to rely on their experience with data to come up with a valid solution to any given problem. This is also a brand new territory for most hiring managers, so they have no idea what skill set they should be looking for, what specific questions to ask throughout the interview process, or how to evaluate candidates’ hard skills.

So if you’re in a position to hire, what should you be looking for? When it comes to making key hires on your data science or machine learning teams, there are a number of things you can do to find the right candidate:

  • Pay attention to problem solving: Any data scientist with technical cred can explain an algorithm, but it’s their experience with problem-solving that really matters. After all, automation for the sake of automation isn’t valuable. You want talent to be able to look at your business and make strategic recommendations for how a machine learning-based technology can solve a pain point for your employees or customers. For example, ask the candidate for a time they applied algorithms to solve a critical business problem that resulted in a positive outcome. The answer will hopefully show you that they not only has technical skills but they also have the business sense to identify opportunities where solutions can be applied.
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  • Host A Competition: Competitions are a great way to identify machine learning talent. They’re an opportunity for machine learning enthusiasts to test out their skills, and for companies to identify the top talent out there. Facebook hosted a machine learning engineering competition last year for the sole purpose of recruiting new talent directly from the competition’s leader board. More than 1,200 people participated for a chance to interview for an open role at the company. While this may seem out of reach for most startups, there are similar things they can do on a smaller scale like hosting a meetup or attending larger startup competitions.
  • Be forthcoming about the job: Because the industry is so supply-constrained, it’s up to you to provide an attractive and specific look at what new hires will be doing day-to-day. On the Hired platform, which connects companies with job seekers, employers who are more forthcoming with information about what candidates will be doing on the job, like specific problems they will be solving and projects they will be working on, have seen higher responses than employers who reach out with a generalized, stock job description.

According to McKinsey’s State of Machine Learning and AI, the estimated total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. As this technology continues to gain momentum and investment in the space increases, the need for machine learning talent will only increase. We’ll see an influx of candidates trying to market their relevant skills to hiring managers and ultimately, the talent supply will eventually catch up to the demand. But for now, the talent holds the cards.