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Machine Learning and The Future of Hiring


The future of recruitment is inherently connected with the future of tech, and as advances are made in the world of machine learning it is important for those in the business of hiring to take notice. So, what is machine learning? How will it change the future of recruitment? And, why should you know about it?

What is machine learning?

Machine Learning is a vast and complex area of data science, impossible to surmise in a few sentences. For our purposes, Machine Learning is the ability for a computer to learn without explicit programming, using pattern recognition. The method involves using a model of sample data which will be used to identify and eventually create predictions or decisions based on it. This method is used to identify patterns in data that might be missed by human error, or would be simply too time consuming. The more it is used, the more the machine ‘learns’ to recognise complex patterns and improves its performance on that specific task.

According to Tom Mitchell from Carnegie Mellon University “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

For example, if we want to train a program to predict, shopping patterns at a busy store (task T), you can run it through an algorithm with data about past shopping patterns (experience E) and, if it has successfully “learned”, it will more successfully predicting future shopping patterns (performance measure P).

What does this mean for recruitment?

The keen-eyed among you will have no doubt about the utility of this kind of data analysis in the world of hiring. So much of recruitment and sourcing involves large data sets with complex sets of variables. The ability for ML to identify trends in industry, and even specific job titles, is invaluable. There is a huge advantage to be gained by easily identifying how long a person has been working in a role, and also understanding other information like the average ‘lifespan’ of an employee in that company. On a larger scale, the ability to predict and prepare for a decline in a particular industry means that recruiters can have a head-start on gaining access to that talent pool. The possibilities are endless, and as ML evolves, so will recruitment.

What about the recruiter? By removing the need for recruiters to identify trends in large amounts of data, time is saved and can be focused more efficiently on the human side of recruitment – building relationships, candidate care, intuition etc. Time is saved on accounting for semantic blunders too, as it eliminates the need to search for multiple titles for roles with similar skillsets, eg. ‘recruiter’, ‘in-house recruiter’, ‘sourcer’ etc. While ML will not select the best candidate for hire, it will present the recruiter with a list of quality and relevant candidates to choose from by narrowing the search, leaving the human side of recruiting to, well, the humans.

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