One of the world’s leading investment firms with 2,000+ employees was eager to develop a more efficient, effective, and data driven early career hiring process that would enable the firm to increase the gender, ethnic and socioeconomic diversity of their summer intern and full time analyst classes. At the start of their partnership with pymetrics in 2017, the firm received approximately 20,000 applications for its vacancies globally, across a range of entry-level job functions in Investment, Tech and Finance. The company urgently needed to innovate its hiring process to adapt to a highly competitive market for diverse talent.

pymetrics invited the investment firm’s current employees within investment, non-investment, and innovations roles to play the pymetrics games. The data collected from the gameplay was used to create custom success profiles that incoming candidates were objectively compared against. Candidates that were “highly recommended” and “recommended” by pymetrics were automatically progressed to the next stage. Throughout, the pymetrics platform aggregated all candidate performance data with a keen focus on assessment completion, satisfaction, and time to complete to provide a comprehensive report to the recruitment team at the end of each hiring cycle.

Candidates found the investment company’s revamped hiring process engaging and easy to complete, as reflected by the stunning 99% completion rate and 95% satisfaction rate. On average, the pymetrics exercises took only 33 minutes to complete start to finish. 

In terms of efficacy, pymetrics-recommended candidates were 97% more likely to ultimately receive an offer from the client than the candidates who were not recommended, allowing their recruiting team to re-focus their efforts and valuable time on recommended candidates.

The most heartening impacts however were on process fairness and diversity. Before introducing pymetrics, the firm sourced a large percentage of new employees from referrals or talent pipeline programs with top local universities, focusing on only 9 universities. With pymetrics, offers were extended to candidates from over 66 different schools. Female representation among recommended candidates increased by 44%, with the client increasing their female representation by 62% from application to offer. Minority representation among recommended candidates also increased by  and 9%, following a study period. 

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