When Even Tech Job Training Lags Behind Need

In any employment market there are going to be jobs in high demand and those that go unfilled.  In our tech driven economy, the jobs that are hard to recruit for range from utility lineman (long hours, hard work, and fabulous pay) and, strangely enough, cyber security.  With all of the hype and news around hacking, I was surprised to learn that these $80k/year jobs are readily available.  But why?

From a selection standpoint, good cyber security engineers need an odd combination of skills.  Of course they need to be great programmers with high levels of critical thinking.  However, they often need to have a criminal’s mindset (“How would I get into this system without someone knowing?”), which makes them a risky hire given their access to sensitive data.  And makes them attractive on the black market.

The incentives for prevention jobs are also difficult.  After all, they are performing well when nothing goes wrong.  But, when someone breaks into the system…

This is an opportunity for industry and universities to work together.  College students want tech jobs (sorry to those of you who recruit linemen), but they tend to want to work in the sexier product/app development area. Tech companies can show higher education how to make the field more “fun,” perhaps through gamification and appealing to the cat-and-mouse aspect of the work.

My sense is that they pay for these jobs will also need to rise to fill them.  If it is true that good cyber security engineers have good hacking skills, there needs to be a sense of doing the right thing pays at least almost as well as breaking into systems.

What we see is that even tech companies need to be thinking about how to get future workers trained and recruited for jobs that are not that appealing.  As our economy constantly evolves, companies will still need “legacy” employees (yes, some day, app development will be boring compared to what is hot then).  And it is possible that the cycle of job obsolescence will become shorter.  This makes the challenge for schools to provide the skills to future employees even greater.  Industry and education will both benefit if they work together in that venture.  I just hope in the meantime no one has hacked my blog.

Putting Tech Diversity Puzzle Pieces Together

I am going to write about an issue with political ramifications while doing my best not to be political, so please accept these thoughts in that light.

One thread going through the proposed ban on legal immigration to the U.S. is the effect it will have on the tech industry.  Those companies are concerned that some of the talent they need from other countries will be unable to either enter the U.S. on an H-1 visa or be allowed to immigrate here.

Another issue that the tech industry has struggled with is hiring a diverse workforce in the U.S.  Much has been made of the lack of women and (domestic) minorities in the tech field.

However, there are more programs to teach tech skills to minorities and girls than you can shake a stick at.  A Google search of “minority tech training” garnered almost 18 million hits and “girls tech training” got 198 million. So, there is not a shortage of opportunities to obtain coding or other tech skills in the U.S. and these programs have created a pipeline of talent.  Likewise, there are specific tech incubator programs for women and minorities who want to start their own companies.

Why is all of this important?  Primarily because for a company to be innovative it needs to look at the world through a window and not a straw.  There are more tech users outside of the U.S. than inside, so to be successful internationally companies need foreign talent.  Shutting our borders and wanting our companies to compete overseas is a difficult problem to solve.  Women and minorities outnumber white males in the U.S., so the companies that harness those perspectives are likely to be the most successful ones.

So, what might be the barriers to connecting talent to opportunity?

  • Hiring like us. I’ve written before about the built in bias we all have of wanting to be with others who have a similar background.  This is very prevalent when it comes to which schools the person attended, which sports s/he played, etc.  This can be alleviated by:
    1. Removing names from resumes.
    2. Removing schools and extra-curricular activities from resumes, unless you have data supporting their use (and the literature on the validity of training and experience measures is not encouraging).
    3. Recruiting where you have never recruited before. For instance, go to schools that are not currently represented in your workforce.
  • Candidates being unfamiliar with the hiring process. In our bubbles we think that every step of the hiring process is normal.  But, to use a tech example, if I come from an area without many tech companies, I might not be familiar with “whiteboarding” code problems as part of an interview.  Being transparent about the steps and letting people know what to expect removes a potential barrier between you and a qualified candidate.
  • Hiring processes that invite bias. Whether it is how you score your interviews or evaluate resumes, having an evaluation rubric will reduce bias.
  • Echo chambers on interview panels. Have diverse points of view (e.g., people from different departments) on your interview panel.  This is likely to encourage meaningful follow-up questions, even within a structured interview.

I do not think that lowering standards is an appropriate response to creating a more diverse and innovative workforce.  Building walls (metaphorical or otherwise) makes the problem worse.  Rather, companies need to build more bridges to find qualified candidates to bring different perspectives to their organizations.  Yeah, I get that it is more work, but a competitive marketplace demands it.

When More is Worse

In every organization you need to strike a balance between implementing good processes and getting things done quickly.  In that sense, recruitment and selection are no different than any other logistics activity.

This article has some one person’s opinions and insights and into problems the U.N. has in how they hire and manage people (among other things).  Obviously, the U.N. has special challenges in terms of security, but an average 213 days to recruit someone for any job is ridiculous.  And, remember, half of the positions take longer.  One can only imagine the use of the same process to hire a grant manager, administrative assistant, and field doctor.
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All hiring is a risk analysis.  What are the odds that this person will work out (not that you can improve them with validation selection tools)?  Call centers and retail always have to balance opportunity costs of having an open position rather than filing it will a less than ideal candidate.

Another issue is the long term cost of having an underperforming employee.  Many organizations, apparently including the UN, do such a poor job of performance management that a bad hire is expensive for a long time.  Again, a case of far too much process getting in the way of achieving organizational performance.

But, things can change.  GE has made a big bet on data.  So much that they are getting out of some of their traditional businesses to focus on it.  What I liked about this article was how they worked from business need (how can we recruit more software engineers?) to their HR processes (OK, we’ll make this change so we can attract more).  Of course, they’re a big company with big legacy processes and I’m sure the article makes it sound a lot easier than it was.  But, they are making it happen.

For more information on making your HR process work for you, contact Warren Bobrow.

A Crazy Way To Test Candidates

You think you have it bad when hiring. Imagine if:

  • All of your entry level job candidates were known to your entire industry and customers.
  • You and all of your competitors had access to exactly the same background, pre-employment, and past performance data, outside of your one chance to interview this person.
  • Oh, and at least one of the pre-employment tests that are given doesn’t correlate with the performance of your most critical employees.
  • The cost of acquiring the labor is huge and the compensation levels are fixed.
  • If you make a mistake, it takes a year to correct.
  • It may be 3 years before you know if you made a good hire.
  • The order of when you and your competitors can make a job offer is pre-determined, though for a high price you can jump the line.
  • And this all takes place on national television in front of your customers.

Welcome to the drafting of professional sports players in the United States. And this time of the year, the focus is on the National Football League (NFL).

I bring this up because the NFL brings nearly all of the prospective players to a group workout called a combine, which leads to the drafting of players in April. In the combine, the players are prodded and poked by medical staffs, given psychological tests, and are put through a variety of physical skill exercises. Teams also have a chance to interview players individually. The combine is organized so that the teams can see what the roughly 300 players can do without flying them all over the country. For players’ perspectives on this and the drafting process, click here and here.

 

The oddest thing about the combine is that they take single measurements of core skills (speed, jumping ability, etc) when they have access to recordings of every single play in which the player has participated (real performance). Granted, different players go against different levels of competition, but you would think that about 1000 samples of a person’s performance would be a bit of a better indicator than how fast he covers 40 yards (usually a touch under 5 seconds, even for the big guys). The interviews can be all over the map with clubs asking about drinking behavior (the players are college students) and the ability to breakdown plays. And then many players get picked by teams that don’t interview them at all.

From a validation point of view, the performance data on players are actually readily available now. Much like call centers, the NFL records some very detailed individual statistics and not just team wins and losses to evaluate players. Whether the number of times a defensive lineman can bench press 225 lbs correlates with tackles for loss is not known (or at least published), but you get the idea.

Much is made about the pressure that the players are under to perform well at the combine. This is probably more so for those from smaller schools or with whom the teams are less familiar. But, the pressure is also really on the talent scouts (sports’ version of recruiters). They only get to pick 7 players in the draft. Undrafted players can be signed by any team and history shows that they have a surprisingly high success rate (see below).

Because of the amount of data available on players, the draft process is reasonably efficient, if you use the metrics of percentage of players who are in the starting lineup on rosters by draft position, turnover (which is mostly involuntary, and achieving high performance (measured by being voted onto the all-start team), higher drafter players do better than lower drafted ones. Of course, the higher a player is taken in the draft, the more he’s paid for the first part of his career, so there is some financial bias to start higher drafted players. Interestingly, undrafted players perform at the same level on these metrics as third round picks. Perhaps there’s something to having a chip on your shoulder.

What we can learn from the NFL is that when there’s a lot of data available, you can make better selection decisions, even when your competitors have the same data. Second, there’s still plenty of good (though not the best) talent available that’s overlooked by the masses. Finding that inefficiency in the selection process and addressing it can lead to a significant competitive advantage. A good validation process can help you do that.

For more thoughts and insights regarding pre-employment test validation, contact Warren Bobrow.

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