In the world of IT recruitment, we often come across two fundamental types of candidates: active and passive. This distinction isn’t just theoretical—it has a real impact on outreach strategies, the speed of the hiring process, and expectations on both sides. If companies fail to recognize this difference, they risk losing top talent, not just due to poor timing but also because of the wrong approach.
A passive candidate is someone who is not actively looking for a new job but remains open to new opportunities. They aren’t actively browsing job boards or applying to roles, but they might be interested if the right offer comes along.
In IT, this type of candidate is quite common—many professionals use networking or occasional job offers to "calibrate" their market value. According to LinkedIn Talent Trends, up to 70% of the global workforce falls into this category—not actively seeking, but willing to listen.
On the other end of the spectrum is the active candidate—someone who is actively searching for a job. They may have already resigned, be in their notice period, or have made the decision to leave soon. These candidates:
A Jobvite study shows that active candidates typically make a decision within 10–14 days of starting their job search. This means that companies with slow hiring processes or overly complex interviews risk losing top talent.
Companies that recognize the difference between passive and active candidates and tailor their approach accordingly will increase hiring success while also improving the candidate experience.
Every candidate is unique, and there is no one-size-fits-all approach. Today, recruitment is more about understanding people than just finding technical skills. The best recruiters know this and adjust their style based on the candidate’s situation.

For decades, the pharmaceutical industry has been locked in a relentless pursuit of new medicines. Drug discovery remains a slow and expensive process, plagued by low success rates. But AI in pharma can write a completely different story. From analyzing large datasets to identify promising new drug targets to streamlining clinical trials, AI speeds up the discovery of life-saving treatments. This article explores the possibilities of AI in pharma, discussing how it can help us win the race against disease and ultimately bring better health outcomes to patients around the world.
Read More
It has been several days since we last met (or e-met) and I couldn’t wait to write another article. This is Jordan from SnapStack Solutions and this week I’ll talk about object-oriented programming, a much broader topic. Quick reminder, last time we talked, we covered Apache’s technologies Spark, Hive, and Hadoop. I guess you already read it, but in case you didn’t, here’s a link to check it out.
Read More
When humans are hurt, their bodies recover on their own. What if technology could do the same? What if we told you it can? Companies are racing to build self-healing systems, which have the potential to enhance quality, save costs, and increase consumer confidence. For instance, IBM is experimenting with self-configuring, self-protecting, and self-healing devices precisely because of this.
Read More