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The Connection Between Tech Recruiting and Machine Learning (ML)

Talent sourcing and recruitment are critical for developing and sustaining your company's workforce. As reported by the Bureau of Labor Statistics (BLS), between 2016 and 2026, the labor force is expected to rise by 11.5 million workers. The increment is roughly equivalent to a yearly growth rate of 0.7 percent.

 

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These statistics demonstrate the rising demand for highly talented and ready-to-work employees. Consequently, there's an increasing need for better and more effective recruitment strategies and tools. For this reason, recruitment agencies and developers have devised new and more efficient ways to source and recruit workers. Machine learning isn't an exception in this scenario. So, what is the connection between tech recruiting and machine learning? Continue reading on to find out the answer to this question.

Use Cases of Machine Learning in Tech Recruiting

Currently, there are plenty of use cases in tech recruiting for the application of machine learning technology. To a great extent, tech recruiting involves collecting information, that is, finding profiles, screening and scoring them, and evaluating candidates, among others.

 

Each phase in the tech recruiting cycle is possibly data-intensive, explaining why there's a connection between tech recruiting and machine learning. The following are detailed use cases that demonstrate the connection between these two aspects.

1. Sourcing and Screening of Candidates

The most prevalent application of ML in tech recruiting concerns the sourcing and screening process of prospective workers. Usually, Machine Learning assists information technology recruiters in analyzing prospects' profiles and, in some circumstances, their social media activity. The report helps recruiters better understand candidates' interests, educational backgrounds, skill sets, and experiences.

 

In turn, tech recruiters can then match the information to the job descriptions of the hiring firm to find the most suitable candidate. Here, the connection between the two aspects concerns the speed and volume at which machine learning algorithms analyze data.

 

However, the major limitation of machine learning regarding tech recruiting's sourcing and screening processes involves performing checks based on requirements. That's because talent data is difficult to standardize since prospects from various sectors have varying approaches to presenting and organizing their details. Likewise, hiring enterprises from different sectors often draft their job requirements and specifications differently. Nevertheless, machine learning has proven useful in aiding tech recruiters in sourcing and screening prospective workers.

2. Assessment of Prospects

This parameter is often used to evaluate a prospective worker's competency level. Mostly, an assessment aims to define a candidate's skills, but it can also involve intelligence and personality aspects.

 

In some instances, it's viable for you to adopt machine learning technology to support candidate assessments. That's because, more often than not, assessing intelligence, skill, or personality can be more sophisticated than a simple logic algorithm.

 

Most machine learning assessment solutions attempt to contextualize intelligence, personality attributes, and skill sets with the job candidates are expected to perform. Often, this is a sophisticated activity because each job comes with distinct requirements, and each enterprise has a unique culture and requirements for specific talent. And this is what demonstrates the connection between tech recruiting and machine learning.

3. Engagement of Prospects

You must keep prospective workers engaged in order to attract their attention to your job advert and enterprise. However, when searching for the right talent to hire, you don't have the time to engage prospects regularly.

 

Thus, you can leverage machine learning technologies, including chatbots that imitate conversations to help candidates learn more about your organization and job post before engaging human recruiters.

 

Powered by ML's Natural Language Processing, chatbots can comprehend human language and construct logical responses to candidates' questions, prompts, and answers. In any case, most machine learning chatbots are based on language models that perfect their learning as they engage users. Some of the most efficient machine learning-based candidate engagement chatbots include Jobpal, Mya, and Olivia. This use case demonstrates one of the most successful applications of machine learning in tech recruiting, indicating how the two are connected.

Conclusion

The amount of data generated in today's business environment is huge. That's why you require a more efficient and faster way to process, analyze, and use it to make more effective recruitment decisions. And that's where the connection between machine learning and tech recruiting begins.

 

Author The Author: Zoya Maryam

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