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Why It’s Time to Modernize the Technical Screen

Why It's Time to Modernize the Technical Screen

Employers in today’s job market deal with intense competition for top technical talent and the challenge only continues to grow as demand for engineers increases. To hire effectively and efficiently, it is becoming increasingly important for employers to shorten the duration of the hiring process and identify strong candidates as early on as possible. Many employers try to do this with traditional technical phone screens, but these often fall short of meeting today’s hiring needs in three key areas: (1) efficiency and cost, (2) fairness, and (3) candidate experience. Modernizing technical screens through automated scoring and scalable evaluation frameworks can help avoid these pitfalls.

Efficiency and Cost

With ever-growing applicant pools, the cost of conducting technical screens is increasing exponentially when accounting for resources devoted to content development, interviewer training, administration, and candidate evaluation. For high volume roles, this cost can quickly become prohibitive towards the goal of screening out underqualified or uninvested applicants early in the hiring funnel. Updating technical screens through standardization of content and automation of the scoring process allows organizations to scalably screen out a large number of applicants at a fraction of the cost in a more accurate, fair, and consistent manner.

Fairness

Traditional technical screens are human-scored, presenting ample potential for bias to impact hiring decisions, as people often hold unconscious or conscious biases. However, when developed, administered, and scored through rigorous and validated processes, technical screens can be more objective and accurate tools for identifying applicant skills, abilities, and other characteristics without threat of human-driven biases. This enables employers to make more consistent, fair, and defensible hiring decisions early in the hiring funnel, which often leads to more down stream efficiencies and positive outcomes such as stronger onsite-to-hire ratios and job performance.

Candidate Experience

Well-designed technical screens provide applicants with initial previews of the type of work and skills required for a position. A technical assessment that replicates the demands of the job is more realistic and face valid than traditional assessment methods, therefore increasing the opportunity for candidates to better understand: 1) why they are completing the assessment or interview, and 2) what to expect in the role. In addition, standardizing and automating processes in technical screens frees up hiring managers’ time considerably, allowing them to focus less on manual evaluation and more on facilitating a positive candidate experience, through activities like discussing the job and the company with candidates.

In sum, modernizing technical screens through standardizing the evaluation framework and introducing automated scoring not only reduces costs and saves time, but also provides more objective metrics to measure applicant qualifications and improves the overall candidate experience.

About the Authors

Seterra Burleson, MS, is an Industrial-Organizational Psychologist completing her Ph.D. at Old Dominion University. She is experienced in conducting research and providing evidence-based guidance in the spaces of workplace diversity, equity, inclusion, and belonging (DEI&B), as well as talent assessment, talent retention, training and development, and leadership development. Seterra is invested in conducting research that informs organizational initiatives to improve employee experiences in a way that supports organizational goals and values.

Nathan Hundley, PhD, is an Assessment Research Manager at CodeSignal. Nathan holds a PhD in Industrial-Organizational Psychology and specializes in assessment development, machine learning/natural language processing applications, and combining the two. Nathan is passionate about establishing and understanding assessment fairness and explainability/interpretability, especially when machine learning and assessment development are intertwined.

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