Just how hard is it to recruit
AI talent?
January 9, 2018 | By Lisa Dare, TEKsystems Digital Content Strategist
Recruiter Cora Player has a tough gig: recruiting artificial intelligence talent.
At its last Symposium, Gartner gave IT leaders a good scare when it exhibited the problem: a huge number of enterprises and startups competing for about 10,000 AI professionals worldwide. The wide imbalance of needed skills and existing supply looked like a serious obstacle.
Other factors add to the difficulty in recruiting AI pros:
- Terminology confusion about AI vs. machine learning, deep learning and data science
- Thousands of different products and open source components can enter the skill set
Player is less fatalistic. “It’s hard to recruit AI and machine learning talent, but they are out there. You might just have to get creative about sourcing and developing candidates.”
Which skills make up AI?
Typical technology skills and domain expertise include:
- Python, R and Java programming languages
- TensorFlow or other open source libraries
- Natural language processing (for machine learning)
- Speech recognition
- Robotics
- Vision-based image recognition, machine vision
- Hadoop or other big data processing frameworks
- Algorithms
- Predictive analytics
- Data modeling
- Domain expertise
It’s that last skill—understanding both general business needs and knowledge of a particular industry—that bedevils companies trying to recruit top AI talent.
“There are plenty of grads coming out of rigorous programs teaching AI and machine learning skills, but companies are reluctant to take a chance on fresh grads,” says Player. “That may change as enterprises start feeling the supply reduction and cost increases, which will lead to more investment in this space.”
Ram Palaniappan, head of TEKsystems’ Data Analytics and Insights practice, agrees. “Many recent grads are well-versed in algorithms and the technical elements of AI, but being able to come up with ways to apply these technologies in building great applications or products is where the gap is.” Palaniappan suggests hiring a mix of less experienced professionals and more experienced ones who can provide mentorship. Another option is to hire experienced professionals and let them upskill alongside existing resources who know your business.
“Those senior AI pros can be a real draw when you’re trying to attract new grads,” adds Palaniappan.
How you can compete for AI talent
Palaniappan says the biggest challenge he faces in recruiting AI talent for his projects is competing with hundreds of startups. “AI and machine learning professionals really want to work on cutting-edge projects and make an impact on products. What they don’t want is to get benched while your company finds work for them.”
Palaniappan advises companies to court candidates by talking about the use cases for AI at your company, how candidates’ work will make a difference and what kind of technologies they’ll be working with.
Chas Bollow, a recruiter who helps Palaniappan staff AI and machine learning projects, agrees that AI candidates are motivated by challenging projects. “The people qualified to work in AI and machine learning really want to do that kind of job—and often, they’re willing to pick up and move. In 2018 we'll need to cast a wide net for AI talent.”
Player’s final tip: Try scouting Kaggle competitions for potential AI talent.
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