- Rick Wilking/Reuters
Abhay Parekh is the co-founder and CEO of recruiting startup Lytmus.
As every entrepreneur knows, an idea is only as good as the team executing it. However, building theright teamis often even harder than finding the right idea. So how does one build a great team quickly?
The most common approach is to treat early hiring like a resume prediction game. The founders decide (or are told) that there are certain variables (for example,college attendedorlast company worked at), that correlate well with future success.
The addressable pool of candidates is simply reduced to those who match these criteria. Resumes become the basic tool of the screening process. The winnowed down pool is then vetted through a series of interviews, and the emerging finalist(s) are offered a job.
The hope is that the combination of resume screening and time consuming interviews will result in the best future employees: ones who will help make the company a big success.
Unfortunately, things don’t usually work out so perfectly. Here are four reasons why:
1. Resume-based variables don’t actually predict as much as they are supposed to
Numerous studies, most notablyone from Google, establish that even something as concrete as the college attended, and college G.P.A. are effectively “worthless” predictors for all potential employees except for recent graduates.
So what about the parts of the resume that deal with work history? Again, one would assume at least those would be very strong predictors for top hires. Well, the issue and reality here is that there are the doers and there are the posers. Distinguishing between these two kinds of employees is hard without very careful reference checks. You shouldn’t focus solely on candidates who have worked at hot companies since you will tend to overlook great people from lesser known companies. You might be better off putting your faith in a person from a lesser known company who is a verifiable doer.
2. Since everyone is going after the same set of people, especially for technical jobs, the candidate pool can often be overfished
Thus the chance that your startup is going to snag a real “star,” is extremely small. You may just end up with someone who barely made the cut, and since the cut was based on variables that don’t predict success, your winning candidate may ultimately be a mediocre performer.
3. Resume-based thinking leads to poor diversity
The danger here is that highly qualified people often aren’t considered at all. There are numerous studies that show there is a high degree of bias introduced by an over-reliance on resumes and a tendency for interviewers to simply look for people much like themselves. Taking ethnicity and background completely out for a moment, in a dramatic demonstration of bias, inone studyresearchers sent two versions of the same resume to employers. One version had a male name and the other a female. Guess which one got a significantly better response from male interviewers!
4. Interviewing well is hard!
People tend to ask standard questions that can be prepared for beforehand. It is no surprise then, that asking brainteaser-like questions (for an engineer these might be standard coding challenges) is just not effective! Thesame Google studyestablished this definitively. Too many interviewers ask “trick” questions that have very little to do with what the candidate would be doing on the job.
So where do we go from here? How do we avoid common pitfalls and improve our accuracy in identifying and ultimately hiring the best candidates? Or simply, what predicts future performance better than the contents of a resume?
The answer, and the single best predictor is the quality of “work sample”. This isn’t just an opinion but verified over and over again in carefully conducted experiments. In fact, on reflection it seems quite obvious. Would you rather fly with a pilot hired based on a multiple choice test or see how they perform in a flight simulator?
Regardless of the position, this means putting every candidate in a situation that mirrors something they would be doing on the job. Once the work product is in, interviewers should heavily weight their evaluation on that result/performance.
While job requirements vary greatly, even for small teams at early stage startups, there are two simple rules that will help drive you to better hiring across any open positions. First, cast as wide a net as you can. Its simple math: the larger the pool the greater the chance of identifying a top hire. Second, keep the evaluation real. Since the single best predictor of job performance is the evaluation of a work sample, the more that sample represents real, on the job, day-to-day work, the stronger the predictor of success.
In the end, resume reliant recruiting is the norm, to be sure. However, it’s also the limp that just about every industry has learned to walk with. Ultimately investing a little structure and objective evaluation into your hiring process from the outset can greatly improve the chances that your startup can succeed. After-all, any good VC will tell you, it’s not just the idea they invest in, but also in the team that will carry the idea forward.