OnPoint For Organizations

5 Mistakes Recruiters Make When Qualifying Candidates

Posted by Marie L. Clark on Aug 17, 2017 11:12:49 AM

Over the past 3 weeks, 27 recent analytics graduates have reached out seeking my assistance landing a job, even though estimates are there are currently between 140,000 and 190,000 open Data Scientist positions in the US. How could these folks possibly have difficulty finding a job? They are in a sellers’ market! I would expect to see a bidding war since currently in the US, we find ourselves in a job market with 6.2 million open jobs amid 7 million unemployed.

I believe my colleague Vin Vashishta, Founder & Chief Data Scientist at V-Squared Data Strategy Consulting, nailed it when he stated “HR is the forgotten link in the data science success chain”, and I tend to agree. Remember when Human Resources was a vital function? Over the past two decades, HR has been stripped down and outsourced, presumably as a cost saving measure.

One of the recent trends is Recruiters being replaced by Applicant Tracking Systems (ATS.) Although I strongly believe that computers have a superior ability to sift through tons of resumes, we are loading these systems with outdated assumptions. We made these assumptions, without much backing in science to limit the number of resumes we received. However, times have changed.

Here are 2 outdated assumptions that need to be challenged to increase the pool of qualified data science and analytics candidates for Hiring Managers:

  1. “Must have a Master’s or PhD in quantitative field such as statistics, applied mathematics, or computer science” – To date, there has been no correlation found between a candidate’s degree and their future job performance. Hiring Managers should be able to consider qualified candidates, regardless of how they acquired their qualifications. What matters is what the candidate can do today, not what they studied back-in-the-day.
  2. “Relocation Assistance Available” – I recognize that companies each have their unique position on whether to allow remote work arrangements. At the present time, Hiring Managers should be able to consider qualified candidates no matter where they reside, especially for hard to fill positions like those in data analytics.

In addition, I increasingly see job requisitions that limit their candidate pool in the following 3 ways:

  1. Seeking a Jack of All Trades – I see job requisitions that are really for two or more distinct positions rolled into one. For example, an organization needs a Data Scientist who can: (1) source & stage data like an engineer; (2) analyze data like a statistician; (3) drive action in the C-suite like an executive; and (4) oh by the way, can you train our employees to understand the value of data analytics? Consequently, it is now quite common for degreed Data Scientists to spend up to 80% of their time doing data wrangling, which is a misuse of their very expensive skill set and a task that can be done by far more junior resources.
  2. Seeking a Strong Leader, who is Hands-on Technical - Most often you are either a doer or a leader. As technology changes frequently, leaders often lack current hands-on skills, and that’s OK. Their job is to lead and the gist is enough.
  3. What happened to the Gig economy? – More and more companies are returning to hiring W-2 employees. This may be a direct response to the perceived talent shortage. However, hiring FTEs limits the flexibility of the workforce. To survive in the new world order, Hiring Managers need to start to think at the task level. As robots and automation begin to do certain tasks, what tasks are remaining? Where I have gaps, will this be an ongoing need that requires an FTE or is it one set of tasks today and then another tomorrow? We have the ability to piece together teams based on the tasks required, therefore we can make a dent in the 6.2 million open jobs with the 7 million unemployed.

To make this all work, Hiring Managers need to be able to identify and measure the skills of their existing staff, along with those of potential candidates.

Topics: Data Science, Analytics, Job, Gig Economy, Remote, Job Automation, Human Resources, HR, recruiter