I did a search on LinkedIn for a “Java Software Engineer” in New York City.
I entered that job title as a keyword (under Job Titles) and LinkedIn suggested that my talent pool was 2,059 candidates. Then I added a skill and my talent pool decreased to 1,956 candidates. When I added another skill, my pool increased. This is the nature of Boolean search. Every candidate that has at least one of the requirements is brought up in the search results. If you want the skills to be additive (X and Y), you need to write a compound Boolean search string rather than just adding the skills from the LinkedIn menu.
I wanted to reduce my targeted talent pool and added “years of experience” range. The pool tanked. The same happened when I added “education requirements”.
It was not clear what I should do at this point. I didn’t know what to change in my search in order to maximize my pool while maintaining the quality of the candidates in my search results.
Was it a specific skill or the combination of specific skills? Was it the years of experience I wanted or the education? The location in which I was looking for talent was the New York metro area, but I was not sure if it included parts of New Jersey and Connecticut. Should I have expanded the location search, or would that bring in irrelevant candidates?
I was willing to compromise on some of my requirements. For example, I could make some of my required skills “important” rather than “must have”, but I was not sure which skills to change. I was not sure which requirement will give me the biggest impact on my talent pool. I had to try it one by one, but I had too many alternatives.
This is a common experience for recruiters who spend hours trying different alternatives for Boolean searches on LinkedIn and other traditional tools.
With the advent of Artificial Intelligence (AI), that should no longer be the case. Technology has been used for many years to identify and optimize intangible value among alternatives being evaluated. This practice of evaluating novel goods is called “Shadow Pricing”. It is the estimated price for something that is not normally priced in the market. Shadow Pricing is inexact, as it relies on subjective assumptions.
Since it is impossible to assign a precise quantitative value to an intangible benefit or cost (such as an increased talent pool or a change in years of experience), AI assigns a shadow price to each parameter in order to evaluate cost versus benefit and optimize the benefits with minimal cost.
Software invented by Talenya can take a job description and assign a Shadow Price to each of the requirements. It can then show to the recruiter or the hiring managers a set of profiles that represent its understanding of the type of talent that they are looking for and solicit their feedback on how qualified each candidate is to the job. The software can then re-calibrate the search based on the value that the user is associating with each requirement, based on their feedback on profiles.
This allows the software to optimize the search and provide the user with possible search criteria that optimize between candidate quality and talent pool reality. It shows the user possible compromises that they may make in order to have a reasonable talent pool to pursue. A click of a button will apply such changes to the search.
When done properly, it is the story of collaboration between Sourcers and AI Bots, rather than a point of friction between recruiters and hiring managers.