.jpg)
Data science is poised to become one of the most in-demand jobs this decade with the U.S. Bureau of Labor Statistics projecting an estimated 19,800 jobs opening up between 2020 and 2030. Employers can better secure top data science talent for their digital transformation by avoiding common pitfalls and accelerating their hiring process. Let’s take a closer look below.
There are many ways to hire data science talent — in person, online, through recommendations, social media, professional networks, and more. But all of these are prone to common hiring mistakes, which waste valuable time and turn away qualified candidates from companies that need them. Here are the common pitfalls in the data science hiring process and how to avoid them.
Data science is a rich interdisciplinary field with several key skills at its core. Assessment, therefore, is understandably more difficult than lower levels of tech hires. Some companies may overlook qualified applicants by focusing on the wrong requirements, such as:
At closer inspection, it becomes clear that none of these requirements are about skills. A lack of focus on skill will result in unqualified or underqualified applicants being considered for the role for much of the hiring process. This mistake results in a slow hiring timeline and even the wrong hire. On the contrary, focusing on skill filters unqualified applicants early in the hiring process. This option applies to candidate screening, skill testing, and final interview stages.
Another common pitfall in the data science hiring process is focusing on skill but using ineffective techniques to test candidates. Reviewing engineers can avoid these mistakes by employing the following best practices in their assessment:
Lastly, a lengthy hiring timeline frustrates both top candidates and reviewing engineers. Poor scheduling, a segmented interview process, and manual evaluation can keep candidates waiting for weeks or months. Reviewing engineers are often drawn from their main duties instead of evaluating only the best applicants.
Technical hiring platforms with dedicated data science skill assessments can help shorten the hiring timeline. Best-in-class platforms offer the following benefits:
By offering an abbreviated hiring timeline, companies can incentivize candidates to take part in technical interviews. Employers with an efficient data science hiring process also afford top candidates quicker placement than competitors.
In summary, though there are plenty of ways to hire data scientists today, common mistakes in hiring still abound. These include lack of focus on skill, employing ineffective interview techniques, and keeping a lengthy hiring timeline. Companies can best secure data science candidates by emphasizing skill assessment, using modern technical interview techniques, and implementing a shorter hiring timeline. Reviewing engineers can also use technical hiring platforms to speed up the data science hiring process and filter in qualified candidates through skill-based technical interviews.
Shorten your data science hiring process with Filtered.ai’s ready-to-go data science challenges, including Jupyter, Python-3, R, SAS, Java-8, etc. We offer automated scoring with a unique rubric based on test cases for data challenges. Interviewers can also observe candidates in action using Live Rooms with video and/or code pad. Check out our features:
Filtered is a leader in skill-based, data-driven recruiting technology. Our end-to-end technical hiring platform enables you to spend time reviewing only the most qualified candidates, putting skills and aptitude at the forefront of your decisions. We’ll help you automate hiring while applying objective, data-driven techniques to consistently and confidently select the right candidates. To get started, contact our team today or register for a FREE demo.
