Il CPO e Vice Presidente di Analytics di Jobrapido Jean-Pierre Rabbath durante la presentazione di Smart Intuition Technology, la soluzione tecnologica di Jobrapido evoluta per il mondo del recruiting, Milano, 15 Novembre 2018. ANSA/FLAVIO LO SCALZO
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Jean-Pierre Rabbath, Jobrapido’s Chief Product Office and VP of Analytics: “How we use taxonomy to match the right person to the right opportunity”

Thanks to AI and to a taxonomy and machine learning-based engine, Jobrapido can infer jobseekers’ interests matching them to the most suitable set of jobs.

Jobrapido is on a mission to take the work out of looking for work, and they’ve developed a secret weapon – called Smart Intuition Technology™. Thanks to AI and to a taxonomy and machine learning-based engine, Jobrapido can infer jobseekers’ interests matching them to the most suitable set of jobs.

This brand-new methodology is applied to all stages of the user journey: from implementing traffic source; to better profiling the Jobrapido community; to enhancing jobseekers’ searching experience. We sat down with Jean-Pierre Rabbath, Chief Product Officer and Vice President of Analytics, the mastermind behind Jobrapido’s innovative approach, to learn more about one element of the technology behind the world’s leading job search engine – taxonomy – and the future of job searching.

1. Taxonomy. Let’s assume that you’re in front of a primary school classroom. How would you explain it?

Taxonomy refers to a structured, hierarchal classification of something. An every-day example of Taxonomy is the way books are organized in a bookstore. First, at the highest level you have the fiction and non-fiction categories. Under the fiction category, you have genres such as legend, comics, sci-fi, mystery and many other genres. Under the non-fiction category, you typically find biography, memoir, reference, textbook and other similar genres. Within each of these genres, books then belong to a subgenre. For example, under sci-fi, there is alien invasion, post-apocalyptic, alternative universe, etc. This Taxonomy classification makes it easy for customers to walk in a bookstore and find what they are looking for more easily.

In a general sense, when you structure raw data, you can then extract insights from that data. We live in a world where availability of data will continue to grow in exponential ways. We can make better use of that data by classifying it in a clear and well-balanced structure.

2. How did you get the intuition to use taxonomy for job searching?

I have been working within the HR tech space for more than seven years, and I quickly discovered that job descriptions contain a lot of valuable information for recruiters, labor market analysts and economists. To group these jobs together and be able to aggregate the insights, it was essential that jobs would be classified to an occupational taxonomy.

Looking at the occupation taxonomy standards that were available, I could not find one that was granular enough to ensure that we could get to actionable insights. So, I decided that we had to build our own granular occupation taxonomy.

3. Can you give us some concrete examples of how taxonomy will improve job seekers life?

The beauty of the taxonomy from a job seeker perspective is that they don’t have to learn what it is or change the way they search for jobs to benefit from it. We apply the taxonomy in our underlying job search engine to improve the job seeker experience and get them to their dream role as easily as possible.

For example, when a job seeker is searching for a job on Jobrapido, where taxonomy has been implemented, the results are more inclusive of all opportunities closely related to the job seeker’s search. The taxonomy matching uses all the matching skills to suggest job opportunities that the job seeker may not have considered.

Another benefit is that because we can group similar jobs in the same occupation category, we can infer the compensation information for that occupation in a given location. This information is very valuable for the job seeker as it will help them in negotiating their salary.

4. How does taxonomy help companies in their talent search?

For companies looking for the best candidates, taxonomy, when applied to job seeker profiles, allows them to target candidates with skills similar to what they are looking for. Taxonomy allows them to enlarge the number of potential candidates with the right skillsets who can apply for the job opening, a very important aspect especially when the job opening is hard to fill.

5. So… Taxonomy is the future of job searching?

Taxonomy is an essential building block. However, the future of the job searching depends more on how companies leverage it to create the best matching algorithms, and how they use data to infer insights on open positions and the jobseeker profiles that are in their recruitment databases.

For recruiters dealing with multiple job requisitions, it will be important to rely on factual data, and less on gut feelings. Taxonomy allows them to get these insights and act on them.

6. What other tech-related developments do you see that are going to influence the job market?

With an industry poised to grow technically, both recruiters and jobseekers are expected to become smarter and more efficient.
The rise of AI in the recruitment space will enable the development of new matching tools, draw correlations, assist in decision making and make predictions. All these tools, if used correctly, can improve the recruitment process and allow leaders to completely redefine the jobseeker experience.
On the other hand, analytics is arming jobseekers with valuable information that is making them more confident during the interview process.
These technologies have incredible implications, and we have only scratched the surface. More importantly, we are just beginning to imagine the real-world applications for their use and companies that will adapt them will be the clear winners.