In the current economic climate, hiring managers face an unprecedented influx of applications for any open role. Companies today receive far more resumes than in the past. Ben Taub, CEO of Dataspace, explains that this trend is driven by two main factors: an extremely competitive technology job market as organizations cut back after bulking up their workforces post-pandemic, and the convenience of online platforms that allow job seekers to apply for positions with the click of a button. The bottom line, Ben says, “More people are competing for fewer jobs.” This leaves hiring managers with a backlog of applications to process and fewer resources to do so.
A little pep talk from our seasoned recruiters
While filtering through hundreds and hundreds of resumes is no enviable task, there certainly are benefits to having a broad pool of candidates. And, rest assured, there are ways to get through all those applicants efficiently and effectively. Remember, as daunting as it may seem, the current “employers’ market” provides an excellent opportunity to find unicorn candidates for your hard-to-fill data science roles.
We’ve shared tips for screening data science job descriptions before. Here are a few more techniques specifically aimed at helping you get through a lot of resumes, quickly and reliably.
The first pass: The art of quick rejections
In an environment over-saturated with applicants, the real trick is to reject quickly. While it may seem ruthless, it’s the best way to cut through the noise. If you eventually find you’ve overscreened your resumes, you can always reconsider those you’ve rejected. It helps to have categories set in your ATS (applicant tracking system) so you know why you rejected a group of applicants and can easily revisit them if you need to.
Take a pass on applicants who leave off basic info
In the age of remote work and online applications, resume fraud has become a frequent problem. Third-party employment houses flood the market with fake resumes and then find an actual candidate to go through the interview later, if they’re offered one. These resumes get easier to spot with experience, but there’s some shockingly basic stuff that’s common: Did they only supply initials for their first or last name, or both? Are job titles or employers missing or generic? Our recruiter, Katie Magill, says, “If the entry does not have a first and last name, most recent job title, and most recent employer, then I reject.” Of course, it may be the case that, while not fraudulent, the applicant was overly cautious or negligent. Either way, this is not a good look for a high-value data-focused position and a safe bet for passing over.
Use application questions as disqualifiers
As you dig into the applications, start your review with the supporting questions rather than the resumes themselves. Use disqualifying questions to filter out candidates who don’t meet fundamental requirements. For example, if your organization is unable to sponsor work visas, posing the question, “Will you need visa sponsorship?” allows you to quickly identify those who would not be eligible for the role. Similarly, if a candidate’s salary expectations exceed your budget, it’s better to know this up front to avoid wasting everyone’s time. And if the answers are simply left blank or single-character responses, you can safely set them aside.
The second pass: Get your priorities straight
You’ve probably made good progress whittling down your applicant pool, but there’s still an overwhelming number of candidates to review. What now? This is where having a well-defined job description can make all the difference. But even if you don’t, there are techniques you can use to suss out the most promising resumes. The most important thing is to prioritize how you review candidates by their likelihood of meeting the requirements of your role. Use your ATS to put candidates into prioritization buckets to help you stay focused on finding the best fit.
Get real about real-world experience
When it comes to prioritizing applications for a deeper review, Katie recommends taking a good look at the most recent employer. Candidates who list a university often have limited professional experience outside of graduate research roles or internships. If you’re looking for candidates with 5 years or more of experience, you can deprioritize these applicants. Katie also notes, “While these are not the first candidates I look at, I often go back and review them later to be sure I’m not passing over any qualified candidates.”
Stick to your minimum qualifications
It’s hard not to have empathy for job seekers when the hiring market is this competitive. But successfully filling your role means staying focused on non-negotiable qualifications. Ben Taub, CEO of Dataspace notes, “It may sound harsh, but the reality is that unqualified candidates aren’t going to get hired anyway.” Avoid dwelling on these applications because it only wastes your time. When you minimize the time you spend on candidates that don’t meet your basic requirements, you’ll be able to save your efforts for the most likely fits for your role.
The third pass: Saying yes instead of no
Up until this point, our focus has been to weed out unacceptable applications. Your candidate pool should now be more manageable, with your most promising candidates sorted for prioritized review. While there’s still a lot to do, working through these resumes by priority will help you stay focused on what matters most so you can select those best suited to move on to an interview.
Start fresh by rereading the job description to be sure you haven’t conflated all those candidate skills you’ve been staring at with the job requirements. Then set reasonable goals and pace yourself, instead of just diving in and trying to do as much as possible. And be sure to know how many candidates you want for the first round of interviews.
Look for trends in your applications
Keep an eye out for trends that can make your job easier. For example, a large corporation in your industry that recently laid off thousands of highly qualified tech professionals might be disproportionately represented in your pool. Perhaps filtering for those candidates will help you more quickly identify top contenders.
Don’t be afraid to rethink the role
If you find it difficult to disqualify candidates at this point, you probably need to get more clarity on the role. While it’s better to have this figured out before you post the job, the reality is, it’s not always possible. The process of screening applicants can give you valuable insights that you can use to further refine your job requirements. And since you’ve been using your ATS wisely to categorize your candidates and rejections, it will be easier to reconsider applicants who may now meet your revised job criteria.
Remember to be a human about it all
We’ve shared strategies to help speed up the applicant screening process, and being laser-focused is part of that process. We’ve mentioned how fraud is rampant in job applications and encouraged you to be quick to reject. But despite all of that, it’s important to preserve the human element of the process. Katie shares, “If I review an application that is not the right fit for the role, but the applicant clearly took the time to be intentional with their answers and cover letter, I will make sure to send them a rejection email letting them know a bit about why they weren’t a good match.” Providing constructive feedback is valuable to applicants in a market where many companies don’t even bother to notify candidates that they were rejected. “It is important to at least give a bit of reinforcement and human connection in an increasingly faceless process.”
It’s a good time to hire data talent
The surge in applicants for data science roles presents lots of challenges for recruiting, but it also means that “unicorn” talent is available and looking. We encourage hiring managers to embrace this unique opportunity to find exceptional data talent for their teams. By using a systematic approach where you know what you need for the role and are somewhat ruthless about sticking to it, you can make an amazing hire that might otherwise not be possible.
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