54 Kellogg Insight Even within data scientists, we need different skill sets. We need people who are really good at natural language processing. We need people who are really good at deep-learning optimization. We need people who are really good at statistics, et cetera. So people start to have sort of differ - ent flavors and expertise. And we also love people from different back - grounds. We have people with a PhD in physics, a PhD in math, a PhD in engineering. We are going make an effort to hire a PhD in social science. Those different perspectives help to open people’s eyes to different ideas and create innovative solutions much faster than before. INSIGHT: Where do you find all the people you need? WANG: There’s no one way; this is something we have to always work hard on. We love having a dedicated HR person. Having someone who actually knows exactly what we do and, roughly speaking, what the relevant experiences and skill sets are has made the hiring process much easier. We also build a number of relationships with training programs, like the Insight Data Science Fellows program, that become a really good feeder of entry-level talent. And we have started to build relationships with local universities. We attract the interest of students, so we can bring them in as interns. INSIGHT: What advice would you have for the version of you three years ago? What knowledge would you want to give somebody trying to start up an analytics capability in a large organization? WANG: It really helped me that from very early on I had a very strong sponsor. He is one of our senior staff, and he reports directly to the CEO. Based on insights from Jing Wang