When we try to find the right candidates, or the right people for our business needs, we usually have to work with search engines and databases that do not quite speak our language. We write something as cryptic as inurl:resume or filetype:pdf (what does this have to do with my job description?).
To make our search tuned to the meaning of a job description we often research and use the title synonyms. There are attempts out there to learn about possible title synonyms and make the search more semantic. This approach may work quite nicely.
An approach that I’d like to bring up in this post is different: I’d like to talk about removing the wrong results. Of course, it’s not ideal, but I’m finding that it may be quite productive.
To give you an example, let’s look for QA Engineers in Seattle on LinkedIn (something we did for a client recently). Let’s say we will use the keywords automation and framework, to narrow down the search. Let’s look at the results:
QA engineer ~15 results
“QA Engineer” OR “quality assurance engineer” OR “test engineer” OR “engineer in test” ~150 results
NOT manager NOT director (and additional keywords: QA OR quality OR test) ~680 results. As always, having some false positives is not a problem if the majority of the search results are what we need.
Looking at the results of the last search we find the right people whose titles are: “QA test consultant“, “Engg in Test“, “SDET II“, “test automation architect”, “QA analyst“, “SDET Lead“, “white box test engineering“, to name a few. None of these titles are synonyms I had thought of initially, so the “NOT” search adds many more results.
It’s definitely a useful technique. One of many, of course.
Nice tip Irina. Are there any recruiting tools out in the market that drills down on large amounts of data to see key correlations?
Thanks for the comment. Not that I know of. It would be quite useful to do that, of course.