Recruitment and Candidate Assessment

Details

Why Is It Important to Have a Good Recruitment and Candidate Assessment Model?

These days, the demand for skilled jobs is very high and, as switching companies every few years has become the norm, it is essential to assure skilled employees (especially in mid–senior level positions) are set up for success. While machine learning will never remove the need for manual screening and interviews, there is a large variety of potential indicators that can help select a successful candidate to become a long-term happy employee from among potentially thousands of applicants. A good ML model can help companies reduce time to hire while optimizing the candidate funnel.

What Internal Data Should I Have for a Good Recruitment and Candidate Assessment Model?

A good ML model for recruitment and candidate selection should involve as much information about current and past successful and unsuccessful hires. A good recruitment and candidate assessment model should have information that would typically be on the candidate’s CV and information about their job performance metrics. Lists of skills, previous positions, responsibilities, and studies are generally helpful.

What External Data is Essential for a Good Recruitment and Candidate Assessment Model?

The key to a good recruitment model requires good natural language processing (NLP) but a great model may require a variety of external data. The most useful datasets are ones that can enrich candidate data with LinkedIn and other professional profiles for cross validation, skill extraction, and general analysis. Of course, background check data can be used to make sure that you are focusing on the right profiles if the position requires it.

What External Data May Prove Useful for a Recruitment and Candidate Assessment Model?

There are many tools that can automatically extract candidates’ social media and online presences to focus on the ones with the right skill-sets, capabilities, and social demeanor. Additional data may include competitive recruitment data and salary data to make sure the position matches salary expectations.

What Are the Main Challenges of the Recruitment and Candidate Assessment Use Case?

There are a few key challenges for the recruitment use case:

1. Legal: any data in the recruitment and candidate assessment use case should be compliant with the legal requirements of the country or state the position is in. These requirements may vary across geographical area and may prevent creation of a “one-size-fits-all” enriched data set.
2. Entity Resolution: it’s not always easy to connect a person’s name to data about this person at scale because a lot more data is required. Most companies overcome this challenge by collecting as much data as possible on the person from first-hand sources, such as LinkedIn profiles and email addresses in order to provide better entity resolution and make sure no one falls through the cracks.
3. Defining Success: there are a few success metrics that can be defined on a company level, but many of the metrics are also defined per role or group—it’s essential to have a good definition in order to build a good training data set or a set of accurate automated heuristics.

Interesting Case Studies and Blogs to Look Into

Oskar Hurme: 10 Machine Learning use cases for HR
The SHRM Blog: Case Study: AI and Bias in Hiring Practices

Tangible Examples of Impact

Using [Google’s AI for HR] program, Openlogix was able to search through more than 30,000 applicants to make a new hire within 24 hours. Previously, it took the firm around four weeks to hire someone. A study conducted by Delloitte found that companies spend 52 days and $4,000 to fill one open position.

The Burn-In: Google announces release of potentially revolutionary recruitment tool