Capgemini is a global leader in consulting, digital transformation, technology, and engineering services, and outsourcing services. In the evolving world of cloud, digital, and platforms, this group is at the forefront of innovation in addressing its client’s issues and opportunities.
Capgemini's strong 50-year+ heritage and deep industry-specific expertise enable organizations to realize their business ambitions through an array of services from strategy to operations.
The conviction drives Capgemini that the business value of technology comes from and through people. Today, it is a multicultural company with 270,000 team members in almost 50 countries.
.avif)
.webp)
Capgemini enables its customers to go digital through an array of services backed by AI, Analytics, and Platform. With a growing demand from its customers, Capgemini looked to expand its Data Science team.
1. Hiring managers spending too much time vetting candidates
The hiring managers are among the top-paid resources at Capgemini. They spent almost 24 hours a month vetting candidates. This hampered project deliverables.
2. High time to hire
On average, it took Capgemini almost one week to screen, shortlist, and extend an offer letter.
3. High competition
Data science being a niche skill, there was intense competition to hire the best candidates.
Capgemini wanted a solution to solve these challenges.
Capgemini was already using iMocha for university and entry-level hiring and decided to try the data science assessments as well. The data science assessments were available in 2 coding languages - R and Python. Each candidate was sent the assessment, and reports of the top-performing candidates were sent to hiring managers.
Since there was no manual review involved, hiring managers could conduct the interviews and shortlist candidates for the HR round. Automated data science assessments meant that the time spent by hiring managers for the hiring process was reduced from 24 hours to just 6 hours in a month.
.webp)
