Machine Learning: Finding the right applicant
Artificial intelligence has been dominating the headlines for years - with stories about robots stealing jobs. Despite all this hype, machine learning has made great progress and companies are striving to use its benefits - whether it be operating a new cybersecurity tool, creating tailored shopping experiences, or using better search functions.
Data shows that machine learning is one of the most sought-after technical skills. Combine this with the shortage of qualified candidates and it's easy to see why salaries in nearly all technical industries are steadily rising. Yet many companies still struggle to find the right candidate for the job.
The many required skills cannot be learned from a textbook, so candidates must not only apply the algorithms they know, but also rely on their experience to generate a solution for a specific problem. This is also brand new territory for most HR managers, so they have no idea what skills to look for, what specific questions to ask during the entire interview process, or how to evaluate the candidates' skills.
When you are in a position to hire employees, what should you look for? If you need to hire important employees for your data science or machine learning teams, there are a number of steps you can take to find the right candidate:
Pay attention to problem solving
Every data scientist with technical knowledge can explain an algorithm - but later on, experience with problem solving is also important. You want talents to be able to give strategic recommendations to your company about how a machine learning-based technology can solve customer problems. For instance, you could ask the candidate about an algorithm they used to solve a critical business problem. You could also ask about any additional potential applications or difficulties encountered when creating it. The answer should ideally show that they have both technical skills and the ability to identify opportunities.
Host a competition
Competitions are a great way to identify talent. On the one hand, they are an opportunity for enthusiasts of machine learning to test their skills and on the other hand for companies to identify top talent. Facebook held a machine learning competition last year to recruit new talent directly from the competition's leaderboard. While this may seem unattainable for most startups, there are similar things they can do on a smaller scale - such as hosting a meet-up or participating in larger startup competitions.
Be open
Employers should openly communicate how employees can work in the future, solve certain problems and which projects will be restarted in the company.
According to a study, the estimated total annual external investments in AI in 2020 amounted to 8 to 12 billion USD, with machine learning accounting for almost 60% of these investments. As this technology continues to gain momentum and investment increases, the demand for machine learning talent will only continue to grow.
