The Problem Might Not Be the Hiring Manager. It Might Be You.

Your talent acquisition team has been tasked with finding someone to fill a tough, high-profile, technical hiring need in engineering or science or information technology. The position is open for a while and your company’s senior leadership is getting nervous because the skill set is urgently needed on a mission-critical project.

Qualified candidates aren’t applying. Significant man-hours are being put into sourcing and recruiting for the role. Finally, an interested candidate is identified whose resume looks promising. She does well on her initial phone screen and is brought in for an interview. Things look good but then comes the hiring manager’s feedback.

“She is close but not quite right technically” or “she has 70 percent of what we need technically but she is missing …” At this point, the hiring manager lists several specific technical skills that the candidate is lacking. Frustrating, right? The first reaction is often to push back and make the case for hiring the candidate anyway and then training them on the missing 30 percent. Maybe you even try to sell executive leadership on making an offer.

We all know that hiring managers can be too picky. There are a lot of people (like Peter Cappelli at Wharton) out there making the case that companies should put more emphasis on training and less on experience (sometimes called the “perfect-candidate” problem). This is doubtlessly true in many cases (Peter Cappelli uses a great example of a cotton candy machine operator job that required previous cotton candy machine experience).

So, going back to my example, it shouldn’t take that long to get someone up to speed on the missing 30 percent, right? Why not go ahead and make the hire? This kind of thinking can be a mistake. The problem often isn’t a hiring manager who is too picky; it’s HR underestimating just how long and steep the learning curve for many technical skills has become. Part of this is related to the usually incorrect assumption that, because many technologies are rapidly getting more and more end-user friendly, the tools and knowledge needed to build them must also be getting easier to master. However, in more cases than not, the opposite is true.

Almost all areas of science, technology, engineering, and mathematics have become more specialized and more complex over the past few decades. This increased specialization and complexity has made learning curves longer and more difficult to master. Biotechnology, control systems, medical device design, propulsion technologies, photonics, antenna design, fluid dynamics, machine learning, signal processing, etc.

The list just goes on and on and on of STEM-oriented disciplines that have grown much more complex and where subject matter expertise has necessarily become more specialized and specific. And you don’t have to dive too deeply into the world of engineering and science to see what I’m talking about. Just look at the computers we all use for work and entertainment.

When I started programming computers in the mid-1980s, most business programs for PCs were written by very small teams or even individuals. Same thing for video games (one person would often do everything from the design to programming to sound, etc.) By 2000, because of increased processing power and, thus, increased complexity, building a game took a whole team and cost up to 4 million dollars. These days, multiplatform games average between $18 and $28 million to develop with headline games often running over $40 million. Why? Because you need lots of people with very specialized skill sets. Skill sets that take a long time to learn and master.

Look at almost any STEM-related business (there are a few exceptions) and you will see the same thing. Higher levels of performance (and ease-of-use is a factor of performance) means greater complexity behind the scenes and requires more specialized and difficult skill sets.

To go back to my example, how long could it take to fill in that missing 30 percent? Years. In some cases, many years.

It’s strange that recruiters generally intuitively understand this when it comes to some skill sets but badly misjudge the learning curve when it comes to others. For instance, when a large medical practice tasks their talent acquisition department with finding a top-tier cardiologist, how many recruiters would try to make the case that they should hire a neurologist (because that’s what is available) and then train that neurologist in cardiology? Absurd proposition, right? But neurologists and cardiologists have very similar academic backgrounds when it comes to their four years of medical school (their undergrad backgrounds are also going to have a lot in common).

The differences in specialized areas of, say, electrical engineering can be much, much greater. And the “real-world” learning curves for many engineers, scientists, and technologists can be significantly longer than in medicine. So, from a business perspective, hiring the “70 percent candidate” can be a big mistake when it comes to STEM work. Instead, more energy should be put into finding a better candidate.

Yes, hiring managers can be too choosey. And there are technical exceptions to my above points (for instance, frameworks like Rails that are designed to have speedy learning curves). Particularly when it comes to hiring entry-level STEM employees, the important thing is having good material to work with (smart, motivated, etc.) Organizations shouldn’t be looking for a perfect fit when it comes to early career hires. But a lot of what I hear recruiters say makes me wince because they so often underestimate just what is involved in learning a particular skill or subset of a technical discipline.

The same is true when it comes to all the talk about not posting job requisitions that focus on very specialized skills or knowledge qualifiers. The move to “generalized” job descriptions that are written from an employer marketing perspective is great, unless your organization actually needs a specific skill set. Then, from both a practical and compliance perspective, it makes sense to be specific in your description. A laundry list of technical requirements will not replace J.K. Rowling on anyone’s late night reading list and your marketing department might prefer a different approach. But if line management requires specific skills to build a product or complete a project, then these skills should be included as position requirements. What is needed is needed.

So how does a talent acquisition professional know when a STEM hiring manager is being too picky versus when they are just being sensible? By having some idea of what is involved in what they are recruiting for, or by partnering with someone outside the stakeholders who understands what is involved. Too many recruiters just throw around the buzzwords without having any sense for the depth of what those buzzwords represent. For STEM recruiters who like to learn, this is good news. STEM recruiters should always be learning more and more about the skills they are recruiting for. And the best ones will enjoy doing so. For those who don’t like learning, maybe they should consider recruiting in a different area. After all, there are cotton-candy machine jobs that need filling.