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 data science hiring process. 

Create The Best Data Science Hiring Process By Avoiding Common Pitfalls

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. The most common pitfalls in data science hiring are not utilizing skill-based hiring, ineffective interview techniques, and lengthy processes. Here are the best practices and ways to avoid making these common mistakes in your data science hiring process.

#1 Utilize Skill-Based Hiring To Avoid Hiring Unqualified Candidates

Data science is a rich interdisciplinary field with several key skills at its core. When analyzing data scientists, the assessment must utilize skill-based hiring techniques and technology. Assessing candidates based on pedigree and not potential can be a death sentence in the data industry. A lot of skilled candidates come from different backgrounds that encourage unique talents. These biggest pitfalls will overlook qualified applicants:

• Hiring Only on Academic Background: While the academic background is important, preferring certain schools, training centers, degrees, courses, and certifications will neglect many high-value applicants that may have different education and backgrounds. After all, a lot of young prospering data scientists are self-taught or have unique training. 
• Hiring Only on Resume Qualifications: While assessing resumes is important, resumes are meant to supply interviewers with information to create better questions about the actual skills assessment. It’s also important not to restrict preferred candidates to those that list the title of ‘Data Scientist’ on their resume because many applicants possess key skills and have performed data science tasks under a different role/title.
• Hiring Only on Years of Experience: Preferring a certain number of years of employment at big-name companies discounts valuable experience and skills that less privileged applicants possess.
• Hiring With Diversity Bias: Preferring certain age brackets, gender, and backgrounds cuts huge swathes out of the applicant pools. Inclusive technical hiring creates larger candidate pools that cover all aspects of unique talent that may be necessary for your data science team to thrive.

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. These pitfalls can also cause higher turnover rates in the future. When candidates are underqualified for their positions, sooner or later, they will get burnt out or let off by upper management. This can even cause issues for qualified employees. If a qualified employee always relies on new employees due to turnover or underqualified employees, they will probably look for a better opportunity. Ensuring stability and quality in technical data science teams starts with a solid data science hiring process based on skills. 

Focusing your interviews on real-world skills in candidates can help filter the unqualified applicants early in the hiring process. This option helps interviewers focus directly on the qualified candidates later in the data science hiring process to determine which qualified candidate best fits the team's dynamics and needs. 

At the beginning of the data science hiring process, the take-home test is the first line for filtering out candidates based on skill sets. Utilizing skill-based hiring technology, such as artificial intelligence resume screening, can help assess the candidates' skills to be tested later. The take-home test can utilize recorded answers to encourage candidates to showcase their soft skills, such as communication and confidence, while also showcasing their technical skills. 

Live-coding interviews can utilize remote environments such as integrated development environments (IDEs) that mimic on-the-job environments to assess a candidate's ability to adapt to new virtual environments and get a taste of the job's daily requirements.

#2 Use Effective Interview Techniques To Avoid Unreliable Results

Another common pitfall in the data science hiring process is focusing on skill but using ineffective techniques to test candidates. Using incorrect techniques can provide unreliable results and potentially point to hiring unqualified candidates. Recruiters can fix these common pitfalls in the data science hiring process with these best practices! 

Common Pitfalls

Best Practices

Using wrong metrics 

Unrealistic expectations, restricting results to only one kind of output, and focusing on less important criteria

Establishing clear criteria 

Candidate output should be measured using a uniform and predefined set of standards 

Allowing room for subjectivity 

Personal criteria for scoring, non-blind evaluation, arbitrary metrics

Implementing objective scoring 

Rubrics for scoring should adequately measure skill and competence 

Using artificial evaluation scenarios Generic tests, random puzzles, multiple-choice tests

Assigning real-life challenges 

Data science tasks should reflect real-life projects and duties on the job such as data cleaning, analysis, data modeling, and others

Inefficient test scheduling 

Candidates wait for their turn to be evaluated by senior data scientists and engineers

Giving take-home interviews 

Multiple candidates can take the test at the same time before evaluation and final interview with senior engineers

Data-driven technology can benefit your data science hiring process techniques. Automatic grading for take-home tests can rid the scoring process of human error, like bias or incorrect results. 

Conference rooms built specifically for saving code to candidate profiles can ensure recruiters have the correct information at the end of the assessment to look over before making the final decision.

Identity verification, authentication, encryption, and authorization with data-driven technology that syncs candidate profiles to other verified resources such as LinkedIn and GitHub can provide more accurate results by dispelling fraud.  

#3 Fast-Paced Hiring With Thorough Evaluations To Avoid High Turn-Over Rates

Lastly, a lengthy hiring timeline frustrates both top candidates, reviewing engineers, and the employees picking up the extra work. Poor scheduling, a segmented interview process, and manual evaluation can keep candidates waiting for weeks or months. While this is taking place, current employees will slowly get burnt out by picking up the slack. This can cause employees to leave slowly and one-by-one open more positions for recruiters to go through the data science hiring process all over again. This decreases productivity and can damage a company’s overall success. After all, the data science hiring process is the backbone for many tech companies. Keeping the hiring process fast-paced without losing quality results can be difficult. 

Luckily, technical hiring platforms with dedicated data science skill assessments can help shorten the hiring timeline and even increase the efficiency of the evaluation. Best-in-class platforms offer the following benefits:

• Multiple candidate assessments simultaneously
• Automated scoring and ranking
• Ready-to-go data science tests
• Live-coding pair programming challenges
• Remote conference rooms built for data scientists
• Virtual whiteboards and IDEs to mimic real-world scenarios… and more

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 a 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. 

Filtered: Sophisticated Skill-Based Technology That Secures Data Science Talent Fast

Finding skill-based, data-driven recruiting technology that includes all of the above requirements and more is difficult to find. Fortunately, Filtered can help shorten your data science hiring process with ready-to-go data science challenges, including Jupyter, Python-3, R, SAS, Java-8, etc. We also 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 a code pad. Check out our features:

• Unlimited interviews
• Data science, machine learning, and artificial intelligence (AI) challenges
• Custom code challenges
• Live interview room with video
• Custom video questions
• Candidate authentication
• Plagiarism detection… and more
  

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. 

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