Machine Learning Increasing The Success Rates Of Hiring Decision

by Recruitment Smart Sniper AI

The success of any organization is directly dependent on the quality of the hire and the association it forges with the employees. After all, employees are the biggest investment for any company and a bad hiring decision can lead to a snowball effect, detrimental to the overall employee return on investment. With so much at stake, talent acquisition becomes a key task in the HR process, with its capacity to tap and evaluate the skills and experience of active and passive candidates, driving the recruitment process.

The hiring decision is based on a very heavy data-driven process, which entails rummaging through millions of profiles, engaging with the shortlisted candidates for an initial round of telephonic interview,s and ascertaining the best fit candidate for the role. The issue with these tasks is that they follow the same pattern of hiring making them more compliance-oriented rather than result-oriented. This is where the capabilities of Machine Learning really shine as it provides ‘perspective’ to the data. It runs through the statistical data and powered by inbuilt algorithms it learns patterns and hones its analytical and cognitive capabilities. Algorithms are insatiable when it comes to data, therefore the more measurable variables you provide the better it will become in making predictions. The prediction of an important performance metric by an algorithm is based on human intuition to decide which characteristics to base the computation on.

The following are a few of the many ways machine learning is changing the standard recruitment landscape:

The repetitive task of profiling, Automated Resume Screening, and shortlisting is handled with ease by machine learning algorithms. They also eliminate unfair bias which might crop up during a human intervention.
The algorithms based on pre-defined variables and patterns learned during data ingestion, goes beyond the keyword matching process and ascertain the viability of the candidate based on past experiences and achievements in previous roles.
The data collated during the automated interview process during initial candidate screening is used to analyze through machine learning algorithms in order to rank the candidates and find the best fit for the job. There are also lots of machine learning software in the technical market that gauges the applicant’s voice intonations and vocabulary and compares it with an organization best performing hire to allow the recruiters to make an informed decision.
Marketplace recruiting solutions, powered by machine learning tools that use natural language processing, semantic analysis to understand the unstructured text of the job description. This data is then used by their associated performance-based machine learning algorithms to find the recruiters with a history of providing the best pool of candidates. This not only enables the employer to work with many specialist recruiters simultaneously but also improves their time to fill significantly while reducing costs.
Machine learning-powered analytical software enables the organization to design relevant and structured job ads based on the patterns that evolved from the previous job ads, making sure it yields more competent hires. The data ascertained through machine learning assist the organization to decide whether it’s fruitful to post ads on social media or on a certain job portal site for a far-reaching effect.

The machine learning capabilities depend upon the amount of data fed into it to decipher regular patterns: more the data, more patterns it will come up with for advanced analysis. Although the advancement in analysis and pattern recognition is unparalleled, the technology is still in its nascent phase, making human involvement still quite necessary.

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