The Evolution of Talent Acquisition with Ana Recio, VP of Global Talent Acquisition at Uber
Ana Recio is currently the Vice President of Global Talent Acquisition at Uber, where she leads a 500-person global talent team in attracting, assessing, and hiring the very best talent through data-driven decision-making.
We sat down and talked to Ana about her innovative insights on the maturation of talent acquisition as a function, with greater investment in cross-functional collaboration, empirical analyses, AI-supported recruiting, and scalable solutions.
The Evolution of Talent Acquisition: Over the years, how have you seen talent acquisition evolve, especially in global organizations like Uber? What shifts have had the most profound impact on the way you approach hiring today?
We’ve seen a big transition from talent acquisition teams just filling reqs (requisitions) to focusing more on influence obtained through market insights, competitive intelligence, analytics, and location strategy. Talent Strategy is much more sophisticated now, bolstering talent attraction as a competitive advantage. As a result, partnerships between Talent Acquisition and the business are much more profound, and everyone is involved. There’s a common understanding now that the whole company needs to get good at hiring, not just the recruiting function.
Recruiting Based on Data: Many companies rely on gut instinct when making hiring decisions. How do you think businesses can use data to take a more strategic, measurable approach to hiring without losing the human touch?
“Determine who your top performers are and what behaviors and traits they embody that allow them to thrive in your organization.”
This is an area where I've spent a lot of time, and perhaps it’s the hallmark of my career! Hahaha. A lot of companies rely on instinct or just look at the competencies of a role, but that only gets you so far. Sure you need to have fluency if you're a coder, or experience meeting quotas if you’re in sales, but competencies can fall short.
We need to think, “What are the behaviors we want this employee to have and how do we want them to show up in our environment?” To find out this information, teams need to conduct empirical analysis: Determine who your top performers are and what behaviors and traits they embody that allow them to thrive in your organization. Once you have that, you can tie these traits to company values and specific employee behaviors—what I like to call ‘success attributes’— that should be assessed during the hiring process.
The analysis does not end once an employee is hired, though. The six-month point of a new hire’s onboarding presents a great opportunity for additional reflection on your hiring process. At this point, we ask hiring managers whether they would hire this person again, if this person is ramping up like you thought they would, and if they have the right attributes. We also send a survey to the new hire and ask if they’re getting the right support and if they’re doing what they thought they were hired for. This is a chance to go back to hiring teams to see if there are patterns—perhaps the new hire isn’t meeting expectations, or your hiring manager could use a bit of coaching. This feedback can also serve as a bit of an early detector for any future employee relations issues.
Analysis-Driven Hiring at Uber: Can you give an example of how you’ve implemented this process in the past and the changes it resulted in?
Last April at Uber, we revamped our hiring process for sales teams to take into account the results of such analyses. We interviewed 244 people on the Sales team, asked them to complete a series of assessments, and reviewed the data with an organizational psychologist. The traits we arrived at included adaptability, a high drive for results, emotional intelligence, and learning agility. We then tied these traits to two main company values: “Go get it” (not taking no for an answer) and “Great minds don't think alike” (diversity of thought, ideas, experience). We found that the behaviors most closely aligned with these values include a high aptitude for risk-taking and being able to work with different kinds of people. We now assess these behaviors in interview questions, rubrics, and scorecards.
Even though it's early, we’re already seeing positive trends amongst new hires in sales. Not only do we see higher quota attainment on the sales team, but also a 21-day reduction in closing a req, a higher acceptance rate, and higher retention at the 6-month mark.
“The six-month point of a new hire’s onboarding presents a great opportunity for additional reflection on your hiring process. At this point, we ask hiring managers whether they would hire this person again, if this person is ramping up like you thought they would, and if they have the right attributes.”
Overcoming Bias: How have you sought to reduce the influence of bias in hiring?
An additional benefit of taking a data-based approach to hiring is that it allows recruiters to look beyond sourcing talent from a few specific companies. Following such a process can dispel a lot of bias and as a result, has allowed us to explore candidates from non-traditional sources such as retail and hospitality. Conducting empirical analysis has allowed us to overcome objections from leaders who only want candidates from specific marquee companies because now we have data to show that individuals for the traditional target company list don’t necessarily do better in our environment.
Automation and AI in Recruiting: With the rise of AI and automation in recruiting, what are the areas you believe can benefit the most from automation, and where do you think the human element still needs to be at the forefront?
We have identified resume markers that indicate whether someone is a strong candidate, so it could be helpful to use AI to go through repositories and extract people who have such markers. To ensure fairness in the process, we also implement safeguards in our AI tools to mitigate bias, allowing us to focus solely on skills and experiences that align with the role requirements.
Additionally, such markers can reveal to recruiters that a candidate may be great for a role that they did not apply for. AI can help identify the reqs and match candidates, even if it suggests pivoting them to other roles. This optimizes our pipelines and makes sure we’re considering everyone holistically, not just auto-dispositioning people.
Building a Scalable Recruiting Infrastructure: For companies that are scaling quickly, what are some of the most common pitfalls in setting up a recruiting infrastructure? How can leaders avoid those mistakes?
One pitfall is that they stop hiring for quality and instead hire for availability. If you are trying to go fast, every candidate is gold, but that often leads you to compromise your comp structure, process, quality bar or representation goals to close candidates to beat out competitors. Even if you want to close as many candidates as possible, you still need to maintain a healthy decline rate that is unique to your organization.
There are various things you can do to make sure you’re not overlooking quality, starting with performance scorecards for recruiters. To ensure that a recruiter is bringing in quality hires, look at 6-month post-hire surveys and whether hiring managers would want to hire that candidate again. If they’re saying no but the recruiter who brought them on has filled 30 reqs, that might be a problem as the recruiter could be trying to push through unqualified candidates for the sake of hiring quickly.
You can start looking at data way before the 6-month mark, though. The candidate experience, for example, can be a very telling metric retrieved from a simple survey that asks candidates to rate their experience from 1-5. Many people send such surveys once a req is closed, which might be months after the candidate initially engaged, or send surveys only to people who got the job. However, I'm more interested in seeing results from people the minute they’re dispositioned at all stages of the hiring process. In addition to the candidate experience, you can ask hiring managers how everything went once a req is filled. What did they think about the recruiter? Did the recruiter bring market insights, diverse candidates, and competitive analysis? Those are just some starting pieces.
We’re grateful to have sat down with Ana and hear her insights on the evolution of recruiting from a transactional process to a strategic, data-driven function. Her approach aligns closely with key trends we're seeing in our client base of designing and implementing standardized hiring processes to hire at scale. Check out more here.
About ModelExpand
ModelExpand is a strategic workplace advisory firm that helps companies put their ideals into action. We partner with organizations to implement, operationalize, and scale their Culture and People initiatives in a way that improves performance across the organization. The ModelExpand team is composed of people from all walks of life. The diversity of the team’s lived experiences, robust industry knowledge, and research acumen fuel ModelExpand’s innovative and tailored solutions. ModelExpand’s work has been featured in Harvard Business Review, Forbes and CultureAmp.
