Jobvite’s New AI Innovation Lab

A couple of weeks ago, we sat down with our CEO, Aman Brar, for a Q&A about diversity, equity, and inclusion. This week, Aman sits down with Jobvite’s new Chief Data Scientist, Morgan Llewellyn, to get the scoop on our new AI Innovation Lab and the team of super data scientists he’s assembling to confront bias in the workplace.

Aman Brar, CEO, Jobvite

Aman: It’s been about a month since you and your team joined Jobvite as part of the Predictive Partner acquisition. What are your initial impressions?
Morgan: The speed! It’s definitely the speed that we’re able to coordinate with other teams and deliver new capabilities. We’ve got several new AI projects kicked off. For instance, we’ve already successfully pitched, developed, and deployed two new algorithms in July to help proactively identify bias within your talent funnel before a job description is even written. 

Aman: That’s awesome. Glad we’re able to match your lightning speed. So tell me about the launch of the AI Innovation Lab?
MorganThe goal of the AI Innovation Lab, or the Lab, is to partner with our customers’ TA and HR teams to help solve their most pressing challenges through the lens of AI and data science. The Lab is the vehicle our data science team uses to coordinate with internal and external teams.

Morgan Llewellyn, Chief Data Scientist, Jobvite

Aman: What kind of services will you be able to provide to our customers from the Lab?
Morgan: There are a few different types of services we’ll be providing out of the Lab. We’re providing rapid delivery services around maximizing the health of your talent pool, reducing bias throughout your TA process, and helping organizations forecast and meet future skills demand and supply. In addition, we’ll be working with clients and partners on a consultative basis to help develop tailored AI-solutions for their highest priority goals.

Aman: When it comes to helping talent teams dig into their data — what do you think will be some of the biggest learnings and opportunities they will encounter?
Morgan: Organizations will be able to not just understand the general TA climate and KPIs but what’s driving those KPIs up and down. It’s a fact that every organization does TA and HR a little differently. This can make it really difficult to understand what’s driving your results and how your organization is doing relative to your peers. By leveraging AI and company–specific data, we can standardize results in order to shed light on what the organization is doing well and provide the recommendations to drive improvement.

Aman: We have access to more data than ever before. What are some of the biggest unknowns for talent acquisition leaders when it comes to that data?
Morgan: Great leaders know their organization’s challenges. Great leaders know the outcome they desire. However, tools, data, and capabilities are advancing and changing at a blistering pace. What this means is that great leaders can struggle with the HOW.

For this reason, partnership between TA and data science leaders is essential. TA leaders have the opportunity to set the vision and leverage data science to build the bridge between challenges and outcomes using the available tools and data.

Aman: Speaking of challenges. We can’t speak about challenges within TA without talking bias. How have you seen recruitment data play a part in addressing bias?
Morgan: How much time do we have because this could take some time! Let’s, first off, say that bias is something that won’t be solved with data and AI alone. Bias is rooted in psychology and manifested in behavior. While imperfect, it’s absolutely important and necessary to work with the data and AI tools that we have and mitigate bias.

What concerns me is simply not having basic diversity and inclusion (D&I) data in the first place. Information is necessary to assess whether a process or piece of content exacerbates bias. If we’re focused on promoting D&I while ignoring the personal and social attributes that comprise diversity, how can we assess whether our process or content is biased? In order to overcome bias, it’s imperative to understand D&I metrics at every level of the process and track the impact of our actions, processes, and even algorithms on the actual and anticipated impact on D&I.

Aman: Can you share more about this team of amazing data scientists?
MorganWe have a deep data science team where the average years of experience is something like 10 years, and many come with a Ph.D. Most of our team has an engineering or computer science background, but we’ve been careful to augment this with a variety of work experiences across SaaS, consulting, government, and research. I think this variety of work experiences is crucial when developing AI for talent as the need for context and understanding the variety of practices is crucial.

I think, too often, we overlook and forget that successful data science is a team sport. At a high-level, we’ll look for opportunities to partner with Jobvite’s engineering, amazing customer service teams, and even customers on research projects for the betterment of TA, HR, and society. It’s when all these groups work together that we’re able to create amazing products and features that truly benefit and enrich our lives.

Aman: That’s awesome, and it’s been amazing to welcome these new Jobviters. How will the market benefit from the AI Innovation Lab?
Morgan: One word: access. Our mission is to provide access to the best data science team in TA so that, together, we can simultaneously advance the capabilities and ethical use of AI in TA.

To learn more about Morgan, read our recent blog. To discover more about Jobvite’s work to mitigate bias, head over to the Jobvite D&I page to see how you can implement a D&I approach that works for you. Or if you want to talk more about how the AI Innovation Lab can help you maximize results from your data just reach out today!