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After discovering the Higgs Boson, can CERN find the best talent within its ever expanding candidate pool?
2 September 2015 • Categories: Clients, Match!, News, Search!
James Purvis, Head of Talent acquisition at CERN, talks about the Nuclear Research Facility’s recruitment needs and how Textkernel semantic recruitment technology is helping attract and source the best talent.
Could you tell us about CERN?
What is the universe made of? How did it start? Physicists at CERN are seeking answers, using the world’s most powerful particle accelerators. CERN is a place where trusted and time-tested technology works alongside state of the art future technology. This provides scientists from the physics community with the tools and conditions to do particle physics research to better understand the universe we live in, and perhaps start to explore the 95% of it we know nothing about. Financed by 21 Member States, it employs just under 2500 staff to serve a community of over 10,000 physicists who come to CERN to carry out their research.
What are CERN’s recruitment needs?
Contrary to what most people think CERN hires employees from a large scope of domains, but very few (less than 3%) research physicists. We need mainly the engineers and technicians and support staff to build, operate and maintain the accelerator complex. We recruit the following profiles:
- Accelerator and particle physics – engineers and accelerator physicists from highly specialised domains
- Cooling – HVAC, hydraulics, cryogenics technicians and engineers
- Civil engineers and technicians
- Electrical – from LV to HV technicians and engineers including superconductivity technicians and engineers
- Electronics, analog and digital technicians, designers and engineers
- Mechanical technicians, designers and engineers
- Firefighters, Doctors and nurses,
- HR professionals, recruiters, L&D specialists, administrative assistants, legal advisors,
- Programmers and software engineers
The main challenge is to break the image that people have of CERN and to make sure we can attract employees from this wide variety of domains. Another challenge is to attract and hire candidates from the 21 member states that contribute to CERN.
“Despite being able to find the Higgs Boson amongst petabytes of data, we couldn’t find a candidate in a few hundred gigabytes.”
Why did you choose Textkernel’s semantic search and matching technology to manage your talent acquisition?
We wanted to be able to see into the wealth of candidate inside our own ATS. We have over 100,000 CVs in our own database yet very limited search capabilities. It was frustrating to know that when you had a new position open there was a good chance the candidate was already registered with CERN. Despite being able to find the Higgs Boson amongst petabytes of data, we couldn’t find a candidate in a few hundred gigabytes.
Every time a need is identified, Textkernel’s search technology is used to browse through our database of existing applications and identify candidates that could be considered for a new opening. Textkernel’s technology is also used to test keywords in searches. These keywords will then be used to write job vacancy. By refining the keywords, we can make sure we are reaching the right audience for a given position.
Beyond that, we also take advantage of the product’s ability to look outside of our database and suggest profiles on, for example, LinkedIn who we can contact for advice or referrals.
“Textkernel’ technology is SUPERB – it allowed us to cross-reference and search for talent that we didn’t even know we had access to”
What has been the impact of the use Textkernel semantic technology in your work?
Textkernel’ technology is SUPERB – it’s opened our eyes to the candidates inside our own database and allowed us to cross-reference and search for talent that we didn’t even know we had access to. Because of Textkernel we have a better understanding of the message we are getting across for a given job description (are we communicating our need effectively?), a better management of our candidate database, a more professional and ‘hi-tech/innovating’ image of our profession and ultimately, the ability to rapidly find the right person for the job and hence reduce the time to recruit.
We are still in the early days of implementation. The next step is to systematise the use of Textkernel for all recruitment. Ultimately, combined with strategic workforce planning, our long term vision is to not only be able to anticipate future recruitment needs well in advance, but also thanks to Textkernel pro-actively propose potential profiles for roles that have yet to formally become job vacancies.
CERN uses Textkernel’s CV parsing, Search! and Match! solutions.
Contact Texttkernel for more information.