Skip to main content

J.P. Morgan utilized Kubrick to gain access to a large volume of data engineering skill – and the innovative thinking of today’s smartest minds. In a 2-week sprint, our consultants delivered solutions to revolutionize diversity and inclusion in their hiring process.

How we delivered rapid proof of concepts to answer J.P. Morgan’s most pressing questions

Kubrick Data Engineers provided the power of a full-scale project team at a fraction of the cost of traditional consultancies to plan, execute, and present meaningful solutions in just 2 weeks.

Data Engineers


3 POCs Built

10 Days

Cost of 1 FTE


KubrickJ.P. Morgan

The challenge

As a world-leading financial institution with a global pool of applicants, J.P. Morgan needed to better understand and attract candidates from all backgrounds and experiences to drive diversity in their hiring process and uncover the potential hidden by typical selection requirements. They required a host of data-driven tools to exemplify how they can overcome barriers to gain a greater and more diverse selection of applicants without supplying their confidential candidate data to a third party.


The solution

A team of 25 Kubrick Data Engineers executed requirements gathering from stakeholders as well as wider project research to hypothesise and build 3 POCs and their prototype tools to cover all stages of the J.P. Morgan’s hiring process. They formed 4 scrum teams to cover each proof of concept as well as create a synthetic dataset using publicly available data from which they could test their tools without requiring access private data from the client.


The results

In a 2-week, agile sprint, the team created a series of tools including: an adaptable data model to highlight the stages of hiring where diversity is most effected, a word-replacement tool to minimise biases in job descriptions and attract underrepresented candidates, an interactive dashboard to contextualise international candidates’ educational experience and performance, and a dataset of 80,000 hypothetical candidates against which to benchmark their own pool. The cost effect of the project equated to $35,000 in resource, delivered at a fraction of the cost and time of a FTE data engineer.

Similar case studies