We also need a very good understanding of the companies. Where they are geographically, size of the company, type of industry, how reputable the company is, how good at recruiting etc. and various other criteria.

The deeper the level of understanding we have on both sides, the better match we can find.

Only once we have a good enough understanding of both candidates and companies, we can strive to find accurate matches.

Of course, there is almost never a perfect match, so we should weigh in different parameters of importance for both the candidate and the company and average over them in a smart way to get an accurate score, measuring the “goodness of match’’. This score should predict how happy the candidate would be to work in a certain company as well as how happy the company (or the HR/recruiting/hiring manager) would be to hire a certain employee.

To explain why there is almost never a perfect match, think about what compatibility is actually based on. It is something more evasive than perhaps we can even put into words. Have you ever gone on a date with someone who on paper seems perfect for you but there’s just no spark? That spark also exists between a company/position and a candidate. Sometimes a job on paper could be exactly what you’re looking for, but you just can’t see yourself happy there. This “je ne sais quoi” or mystery factor is pretty much what we’re aiming to find.

Moreover, close to perfect matches can’t be made without knowing the actual priorities of the users. In other words, that spark will be different for every user and the more we know and truly understand about the user, the closer we can get to identifying that spark.

To illustrate the difficulty in this process, here’s an example from everyday life: Liam is not the best Java developer in the world, but he’s a great team player and stays late at the office as long as it takes to get the job done. People love him. On the other hand, Karen is an expert Java developer, but our data tells us that she’s not really a team-player. Weighing in the different factors when deciding who is a better fit for a specific Java developer position is not easy.

Solving problems that aren’t really defined … now how to solve those problems…

In order to solve this and other similar issues, we use machine learning algorithms. Machine learning algorithms can actually learn, much like an infant would, based on the knowledge of experts in HR, recruiting, and hiring, training the system how they would find good matches among different candidates and companies. In its infancy, the algorithm performs quite poorly, but as time goes on, much like an infant, it matures, and improves performance, and after seeing enough training sets, machine learning algorithms can beat any expert.

In order for your machine learning algorithms to grow up well, you need to be a good parent. As parents we need to give them the right toolkit in order to train themselves instead of just teaching them how to copy us. So that when they have to choose a path, they don’t need to call mommy and daddy and ask what to do because they already know that decision making process.

In fact, today’s machine learning algorithms can beat any expert in chess, GO, and pretty much any strategy game you could think of. In sum, once our algorithms are trained enough, they do much better than experts.

Therefore, once we have enough candidates and companies, and nourish our algorithms with all the data that we have, we can do better than any expert.

But this isn’t enough for us.

Our ambitions lie far beyond this. Our goal is not only to find jobs for candidates and employees to fill in positions. That’s just the beginning.

The Goal:

Recent studies show that employees today are completely different from their counterparts a decade ago. They change jobs frequently, preferring a challenging job over a more secure one, and are more likely to change professions or add new trades to their craft.

Ten years ago, academic background (degrees, subject of expertise, university esteem, etc.) played a crucial part in determining an employee’s worth. As the job market evolved, we see that more and more people are self-educated, and that companies care more about professional experience, soft skills, and other subjective criteria. In other words, top companies don’t just looking for MIT-graduate software engineers with perfect GPAs.

The demand for software engineers is very high and there are other parameters far more important than GPA. Nowadays, many software engineers haven’t formally studied software engineering for an academic degree. They might have taken a few courses in some private school, college, or online — but they are essentially self-taught programmers and the future of software engineering is certainly going in that direction.

It’s much harder to evaluate a candidate based on less standardized criteria.

Moreover, the strongest candidate by the traditionally “objective” criteria might not be the best fit for many jobs. As the job market becomes more volatile and ever-changing, people find themselves in greater need of expert advice on how they should plan their careers.

We are not just telling you which job to take now or even in five years, we might be telling you you should take a medtech online course so that in five years when the medtech industry explodes, you’ll have a place among the greats.

This is exactly what Workey is aiming to do. We’re sitting on mountains of data and preparing to help guide you through a job market that is most likely going to look completely different in ten years.