Choosing the best candidates for a job position

Select more accurately and choose the best candidates for a job position

Companies

Employment agencies

Job Boards

Inda helps recruiters who need to find, in a practical and accurate way, the right candidates for a certain job position. Difficulties related to hiring the ideal candidate can arise at different times or activities. The ad writing process, for example, can be complex and lead to ineffective ads, especially when the recruiter does not have the specific skills needed to write a job description for a certain job field. Similarly, screening candidates to contact for interviews can also become an expensive process especially with traditional tools and not based on semantic reasoning, with the risk of losing all those good candidates who didn’t use the specific keywords sought. Inda’s artificial intelligence helps recruiters to write an effective and searchable job ad, automate the screening phase , in some cases even avoid the ad publication, with the effect of finding the right candidate for their ads more easily. Let’s see in detail how.

Editorial suggestion on the announcement

When you are writing a job ad, you can modify its text by using the skills and job title suggestion to make it more effective, so as to improve its visibility towards candidates on all the channels used for sharing.
Inda suggests alternative skills and job titles to those included in the draft ad and, at the same time, expands the recruiter’s vocabulary by providing synonyms and related words that can refine the text and give better results in terms of visibility. For example, for the keyword sales, the system suggests, among others, salesperson, sales manager, area manager, business developer, etc.
A further help comes from Inda’s ability to recognize and associate skills and tasks of candidates recognized by standard taxonomies such as ESCO (European Skills, Competences, Qualifications and Occupations), ISCO, ISTAT, O*NET.

Assisted search for candidates in the database

Starting from the draft of the job announcement that the recruiter has written, Inda’s intelligent system is able to recognize the skills and the type of candidate sought and propose profiles already present in the company database , which meet the requirements and are qualified for that position.

Data collection for a data-driven approach

Once the job ad has been published, it can be uploaded and stored in Inda’s database and then retrieved for future searches.

It is also possible to save and associate to the job advertisement all the historical information about the applications collected and the step reached by each candidate in the hiring pipeline. This data will allow the development of more advanced and data-driven predictive analysis and editorial support systems.

Fast application with auto-complete

The auto-complete form system allows candidates to register for job openings with a simple click of a button, starting from the upload of a CV file of any format, thanks to the Information Extraction and Named Entity Recognition (NER) methods.

Automated compilation reduces application time and candidate abandonment rates and improves the Candidate Experience. Similarly, recruiters gain much more information about candidates faster than with manual collection methods.

Intelligent classification of candidates

Ad-candidate matching allows you to sort candidates based on relevance (based on skills, job titles, level of education and experience, etc.), assigning each candidate a semantic score. This speeds up screening and allows you to identify the most suitable profiles for a job position.

Candidates' insights

Inda’s Evidence system offers a detail of the score scored by the candidate during the matching with a specific ad. In order to assess compatibility, the recruiter can consult the details of the score achieved by the candidate for each requirement and characteristic they have achieved in relation to the requirements of the ad.
Then, after finding the candidates most in line with a certain ad, for example of an accounting figure, the evidence system allows to drill down on one or more candidates to obtain a percentage of compatibility for each requirement of the ad, while reporting the keywords present in the candidate’s CV that are semantically most in line with that requirement. For example, the system might give the candidate a 99% score on the “invoicing” requirement, reporting the keywords “invoices,” “payments,” “financial statements,” and “suppliers” (note the absence of the exact match).
This allows the recruiter to easily identify the candidates’ strengths related to their skills, but also their weaknesses in relation to the job position in question, making the selection process more transparent.