A recent exchange with Kishan Kokal about a new SaaS application he is developing sparked a familiar line of thought. Kishan's idea is ambitious and modern: a search engine that allows recruiters to input a job description in natural language and receive a ranked list of candidates, complete with a "fit score" and the reasoning behind it. The system cleverly avoids the need for a proprietary database by indexing public LinkedIn data.
On the surface, this is the kind of efficiency gain that technology promises. As I mentioned in my reply to him, a tool that can intelligently sift through thousands of profiles to present a top-ten list is undeniably useful. It addresses a major bottleneck in the recruitment process: the sheer volume of initial screening.
However, this is where the real challenge begins, and it's a problem I've grappled with for years.
The Keyword Trap
The fundamental weakness of such systems often lies in their reliance on matching keywords between a job description and a resume. Job descriptions are frequently inflated with jargon, while resumes, as I often say, can be masterpieces of "Application of Imagination." A clever wordsmith can easily tailor their resume to match the keywords of a dozen different roles without possessing the core competencies for any of them.
If a tool stops at keyword matching—no matter how sophisticated the algorithm—it risks becoming an "also-ran." It speeds up a flawed process but doesn't fundamentally improve the quality of the outcome. It finds better keyword matches, not necessarily better candidates.
A Problem I Foresaw
The core idea Hemen wants to convey is this — take a moment to notice that he had brought up this thought or suggestion on the topic years ago. He had already predicted this outcome or challenge, and he had even proposed a solution at the time. Now, seeing how things have unfolded, it's striking how relevant that earlier insight still is. Reflecting on it today, he feels a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context.
Years ago, I conceptualized a MATCH INDEX ALGORITHM to move beyond simple keyword counting and introduce a more nuanced scoring system. But even that was just a step. The true leap, as I see it, is to generate a far richer, multi-dimensional profile of a candidate. I explored this in a post on how Job Candidate Assessment [can be] Made Easy, envisioning graphical profiles that map competencies, experience, and potential in a way that a simple score never could.
Seeing the direction of new tools like the one Kishan is building, it's striking how relevant these earlier insights still are. The future of recruitment technology must go beyond syntax to semantics, beyond matching words to assessing true capability. I admire Kishan's initiative and technical approach, but the ultimate value will be determined by how deeply his tool can see past the polished surface of a resume.
Regards,
Hemen Parekh
Of course, if you wish, you can debate this topic with my Virtual Avatar at : hemenparekh.ai
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