Are Recruiters The New Data Scientists?

Data use in talent acquisition is everywhere

It’s not news to anyone that the war on talent is fierce. Attracting and retaining the best people is becoming increasingly difficult. Recruiters have their work cut out and it is no wonder that organizations are turning to analytics to help attract talent.  People data and analytics enable companies to make better predictions for future needs, help with diversification of the workforce and provide new methods to identify qualified candidates.

Recent LinkedIn research shows that 64% of recruiters and hiring managers rely on data at least some of the time when making a hiring decision. Looking ahead, 79% of recruiters and hiring managers report that they are  likely to be using more data to support talent decisions in the next couple of years.

While the increased use of data is a trend influencing hiring processes for as many as 50% of global talent acquisition leaders, many recruiters and HR professionals generally feel less than confident in their own data analysis skills. This is most pronounced in the UK, where only 21% of British HR professionals surveyed by the CIPD felt that they are confident working with advanced data, while only 6% use such skills in practice.

What are the qualities of fantastic recruiters

When thinking of the attributes that make a great recruiter, some obvious qualities come to mind: confidence, approachability, drive. Recruiters are expected to be goal-driven sales people, who are self-assured and personable communicators. They are also thought of as an organization’s brand ambassadors.

Rarely do people think of recruiters as being highly inquisitive, data savvy and skeptical. These traits more closely describe the profile of a data scientist rather than a recruiter, yet with the changing face of talent acquisition, recruiters need to make a step change improvement in their analytical skills.

What are the differences between recruiters and data scientists?

To better understand the differences between recruiters and data scientists, we carried out a comparative analysis looking at what personality traits between the two populations. The research focused on a sample of 2,070 Good&Co app users whose job titles were either recruiter or data scientist. We grouped users by their job title and carried out mean comparisons, correcting for multiple comparisons to ensure the accuracy of our results. Please note, most of our recruiter users are in-house practitioners rather than 3rd party agency recruiters.

Our findings suggest that in comparison to recruiters, data scientists are highly motivated adventure junkies. Data scientists are 11% more driven by challenges, they are also 13% more adventurous, 15% more thrill-seeking and a whopping 28% more energetic than the average recruiter.  

On the other hand, compared to data scientists, recruiters are personable and routine-loving. Recruiters are over 25% more cautious and 16% more averse to ambiguity than the average data scientist.  As expected, recruiters show better communication skills, scoring 19% and 14% higher on sociability and persuasiveness respectively.

What can recruiters learn from data scientists?

It may be that recruiters’ prudence and preference towards having complete clarity is inhibiting their confidence of working with data and becoming more scientific in their approach. The points below propose four actionable insights for recruiters that can help in getting an edge over their competition:

  1. Embrace novelty – Recruiters can take a leaf from the data scientists’ book and try to embrace novelty whenever possible. While adventure and thrill seeking may not be recruiters’ natural disposition, keeping an eye out to new developments and an open mind to new methods and approaches can help in identifying a broader spectrum of talent.
  2. Embrace ‘the new’ – in a changing world, it is imperative that recruiters cultivate a positive mindset for the new. This means consciously moving to operate outside the comfort zone of established routine. Creating dedicated time in the day for new skills learning would be a great way to do this and opens the possibility of finding competitive advantage by uncovering new ways to discover in demand talent
  3. Double down on Recruiter strengths  – Recruiters are trained to ask great questions – this can be a powerful diagnostic tool in the assessment of novel techniques which might be uncovered by new skills learning. Combined together this could become an efficient way of ‘pipelining’ new methods / tools from idea to implementation.
  4. Become the ‘data storyteller’ – Communication is the No1 skill for any recruiter. The opportunity exist to use your social skills and understanding of how others think and behave to tell the story around the data you may be presenting, and provide actionable insights based on that story.
  5. Choose your hiring tools wisely – Use tools such as Good&Co’s TeamWork Pro to hire smarter. Let the product identify candidates that are compatible with your team, with popular job titles, key strengths of your high performers and how they will contribute to the culture of the team.

What other ways can you think of that recruiters can learn from data science? We’d love to hear from you if you have any thoughts – comment below or write to us with your idea at

Good&Co is a platform that identifies candidates that are compatible with your team, with popular job titles, key strengths of your high performers and how they will contribute to the culture of the team. We help job seekers, employers, and employees to find a great fit. Contact us for a demo here.

This post was written in collaboration with Recruiting Brainfood

The co-author or this piece was Dr. Roni Mermelshtine, Psychometrics Manager

About Author: Roni is a psychometrician on Good&Co’s London team. She recently completed a PhD in developmental psychology at Birkbeck, University of London. Her skills include applying advanced quantitative research methods using SPSS and Mplus, development of psychometric instruments, and conducting naturalistic observations.

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