Regardless of the macroeconomic climate, hiring top-notch technical talent has always been a challenge. Even though several notable tech leaders made headlines because of layoffs during the latter part of 2022, there’s still a shortage of talented engineers and developers — particularly those with capabilities in data science, cloud engineering, and DevOps.
The technical hiring process is a crucial step to acquiring that elusive talent. Talent acquisition and hiring teams need a skills-based hiring process and a toolset that helps them efficiently identify and hire top talent. And equally important: Companies need to offer a positive hiring experience that encourages top candidates to complete the process — and ultimately accept positions if offered.
Given the high stakes, we wanted to better understand the technical hiring process from the candidates’ point of view. So, we recently fielded a survey of nearly 200 software engineers, data science/AI practitioners, and IT specialists, asking them to share some of their recent experiences and pain points when interviewing.
The survey’s key findings include:
Our data shows that today’s technical hiring process isn’t working well for many candidates. Understanding what candidates want and what they dislike about today’s interview process is what inspires us every day to build a better technical hiring platform. This includes our creation of job simulations to replace broken coding tests.
Here’s an in-depth look at what we learned from our survey — along with some takeaways for talent acquisition leaders and hiring teams:
Developers are masters of building tools and writing code to make processes more efficient — and often share code samples with peers or in online repositories such as Github.
Most coding tests are generic, assessing for basic skills, making answers easily searchable and rendering them useless as a measurement of skill.
In our survey, more than half of developers say they know someone who’s cheated on a coding test — whether via a simple Google search to find others’ code samples, or using AI tools to generate code that can’t be traced back to any online source. With the rise of tools such as OpenAI’s ChatGPT, this problem promises to become more prevalent.
Takeaway: HR and hiring teams should use technical assessments that are tailored specifically for the job, along with using tools that simulate the working environment at their company. They should also consider using technology that discourages or detects code plagiarism.
While cheating on code tests is a problem for hiring teams, candidates don’t like basic coding tests for other reasons.
In our survey, more than two-thirds of tech job candidates said that either coding tests were a waste of time (11%) or that, even though they may help show some coding skills, they weren’t useful in determining whether they were a good fit for a job (56%).
When asked to share stories about interview nightmares, one candidate told us their coding test was “automatically reviewed by an external company with questions that were irrelevant to the position I was applying for.”
Takeaway: Technical assessments are an important part of determining a candidate's qualifications. But hiring teams need to ensure skills-based tests are both relevant to the job and simulate the work environment. Job simulations provide real-world experiences by mimicking real tasks a candidate may complete on the job.
Before anyone takes a job, it’s important to understand the tools you’ll be using for your work. For engineers and technical workers — the builders of the digital age — that’s even more true. When asked to choose their most important criteria for accepting a job, 56% said “having clear insight into the tech stack and day-to-day work” was more important than getting a tour of headquarters, learning about company culture, and even meeting teammates.
As one candidate offered, “I hate it when interviews don't let you work in conditions similar to real life.”
Takeaway: Hiring teams should not only be transparent about the tools candidates would use on the job, but ideally integrate some of those tools during technical assessments in the hiring process.
When asked what the biggest mistake is that companies make when it comes to hiring, the leading answer among respondents was that relevant skills for the role were undervalued compared to education and past work experience. Candidates prefer to be evaluated by their skills and potential performance in a specific job environment, rather than their pedigree.
Takeaway: A skills-based approach to hiring is better for both the candidate and the company.
Use tools and perform assessments that can showcase skills that are most closely related to the job at hand.
We asked candidates what factors had caused them to withdraw from the hiring process in the past. And the most actionable insight relates to candidate confusion about the role. Some 41% of respondents said they didn’t have a solid understanding of what the day-to-day of the job would entail, while 44% said the position didn’t match the original job description they applied for.
Takeaway: Every part of the technical hiring process should be closely aligned with the position. That starts with having an accurate description – and includes ensuring skills assessments are appropriate for the role. There shouldn’t be extraneous coding challenges or interview questions that are off-topic.
One of the most common complaints from candidates is that the hiring process for technical jobs simply takes too long. A recent LinkedIn study found that the median time to hire an engineer is close to 50 days. Yet 84% of technical job candidates from our survey expect an interview process to take four weeks or less, and 57% think it should take no more than two weeks.
Long hiring processes are often caused by the delay of having several rounds of live interviews — and the related scheduling that must take place. In fact, 26% of candidates in our survey said that three rounds of interviews would make them withdraw from consideration, while another 51% said four rounds would definitely be too many.
Takeaway: Talent acquisition and hiring teams need more rigid processes and better tools to shorten and streamline the hiring process — and acquire top talent.
Given that most candidates for technical jobs want shorter hiring processes, it’s not surprising to find out that they would approve the use of automation to help expedite things. Our survey found that only 16% of candidates opposed using automation at all. For those who approve, reasons why included if it increased the chances of being considered (48%) or removed bias (41%) in the hiring process. Some 29% said they were okay with automation as long as the company was “clear upfront that they’re using (automation) and explains how.”
Takeaway: Talented engineers and developers are often hired to build technology that can make different processes better or more efficient — so they appreciate the use of automation technology to make the hiring process better. And talent acquisition and hiring teams could certainly use it to help speed up the hiring process and ensure they don’t lose out on that top talent.
Filtered is reinventing hiring for technical talent. With Filtered, hiring managers can more quickly and accurately assess candidates’ hard and soft skills, helping them move faster with confidence in a competitive talent landscape. Founded in 2018, Filtered is the first platform to offer job simulations, allowing candidates to complete challenges with the exact tech stack they’d be using on the job. The Filtered platform combines coding assessments, recorded video responses, automated grading, and live video interviewing to streamline the technical screening process. Leading brands like Procter & Gamble, Informatica, and Rocket Mortgage use Filtered to hire software engineers, data scientists, DevOps specialists, and more, enjoying on average a 4x faster time-to-hire and 2x better interview-to-hire while saving thousands of hours of interview time per year. Filtered is based in Boston and backed by Andrew Ng’s AI Fund, Silicon Valley Data Capital, and TDF Ventures. For more information, visit www.filtered.ai.