Agile Talent Acquisition Using a Capacity Building Model

The Next Practices Weekly call series has become a well-attended and wide-ranging discussion for HR leaders each Thursday at 11am ET / 8am PT. On this week's call, i4cp's Managing Editor and VP of Research Lorrie Lykins, and i4cp Senior Research Analyst Tom Stone, facilitated a conversation with special guests Grace Niwa, Vice President of Global Acquisition, Talent Intelligence & Early Career, and Kristine Mayle, Ph.D., Director, Global Talent Intelligence, Sourcing & Insights at Vertex Pharmaceuticals. Here are some highlights from the call:

  • At Vertex, they have a lot of data -- but they wondered how they could use it to be more proactive in their talent processes? One challenge they had was: How do you create more agility and efficiency with a lean and specialized talent acquisition team?
  • Their approach was to identify the knowledge and expertise across the TA team in these areas: functional area expertise, disease area expertise, underrepresented talent engagement, global recruitment, early career, and a competitors and biotech peer index.
  • This allowed them to see the team capabilities, with aggregated data revealing strengths and potential gaps.
  • It also enabled them to see specific recruiter capabilities, e.g., who knows the competitive landscape best, who has specific therapeutic expertise, and who has the most knowledge of each business function.
  • The next question they asked was "How can we understand the capacity of each recruiter and increase productivity across the recruiting team?"
  • The solution was to create a capacity building model across teams using machine learning. They intuitively knew that not all reqs are created equal -- some take more time to fulfill, involve special nuances, etc. So what if each req was assigned a difficulty score? They created a capacity model using data from the last 3 years-worth of data they had in Workday. Key factors that were identified and tested included:
    • Job family
    • Level of role
    • Recruiter difficulty rating
    • Historical data volume
    • Average number of applicants
    • Req status
  • This approach to determining capacity and assigning new reqs helps to prevent burnout by having a more thorough understanding of team utilization levels. For a 2.0 version of their capacity building model they will work to improve predictive capability with machine learning, automation, and integration. Doing so will move them away from a traditional team structure (manager/team members) to a more agile team structure that is self-organizing.
  • They've also tackled a third challenge with the use of people data. Overall diversity in the CFO organization looked strong, but when they double-clicked, they found teams were not as diverse in terms of gender. Their approach was to create an ID&E Recruitment Scorecard to help hiring managers proactively analyze their impact on hiring diverse teams.
  • To do this they determined a benchmark of 42% women based on several labor market data sources including Talent Neuron, LinkedIn Insights, and Seek Out. They then created a what-if tool that could be used to show the impact of various hiring changes towards approaching the target benchmark. This benchmark is the floor, not the ceiling, as diversity is a high priority at Vertex. The key here was the ability to show data the business can understand.

Links to resources shared on the call: