 Live from Stanford University, it's theCUBE. Covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE Media. Hi, and welcome to theCUBE. I'm your host, Sonia Tigare, and we're live at Stanford University, covering the fifth annual WID's Women in Data Science conference. Joining us today is Yashu, the head of data science at LinkedIn. Y'all, welcome to theCUBE. Thank you for having me. So tell us a little bit about your role and about LinkedIn. So LinkedIn is, first of all, the biggest professional social network where we have a massive economic graph that we have been creating with millions, actually close to 700 million members and millions of companies and jobs, and of course with students of skills and also at schools as well as part of it. And I lead the data science team at LinkedIn, and my team really spans across the global presence that LinkedIn's offices have, and yet really working on various different areas that both thinking about how we can iterate and understand and improve our products that we deliver to our members and our customers, and also at the same time thinking about how we can make our infrastructure more efficient, thinking about how we can make ourselves a marketing more efficient as well. So really span across. And how is the use of data science involved to deliver a better user experience for users of LinkedIn? Yeah, so first of all, I think LinkedIn is a, in general we truly believe that everybody can benefit from better data, better data access in general. So we certainly, we're using data to continuously understand better of what our members are looking for. As a simple example is that whenever we launch a new feature, we are not just blindly decide ourselves that this is a better feature for our members, but we actually understand how our users react into it. So we use data to understand that and then certainly making decisions and whether we should be eventually launching this feature to all members or not. So that's a very prominent way for us to use data. And obviously we also use data to understand and just even before we're building certain features, is this sort of feature that's right, feature to build. We do both survey and understand the survey data, but also at the same time understanding just user behavior data for us to be able to come up with better features for users. And do you use A.V. testing as well? Oh, absolutely, yeah. So we do lots of A.V. experiments. That's what I was not trying to use that word by that terminology, but this is what we use to have an understanding of is the features that we are developing that we are putting in front of our users. Is that what they enjoy as much as we think they would enjoy? Right. So you had a talk today about creating global economic opportunities with responsible data. So give us some highlights from your talk. So first of all, at LinkedIn, we truly believe in the vision that we are working towards, which is really creating economic opportunity for every member of the global workforce. And if you're kind of starting from that and thinking about that is sort of the axiom that we're working towards, and then thinking about how you can do that, and then obviously the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us. So how can we do that? We have some early effort in using and developing differential privacy as a technique for us to do a lot better with regarding preserving that privacy as we're leveraging the data, but also at the same time, it doesn't end there, because you're thinking about creating opportunity. It's not just about it's preserve the privacy, but also when we are leveraging the data, how can we leverage the data in a way that is able to create opportunity in a fair way? So there is also a lot of effort that we're having with regarding how can we do that, and what does fairness mean, what are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop a product, the way that we're thinking about responsible design, and the way that we build our algorithms, the way that we measure in every single dimension. And speaking about that bias at the opening address, they mentioned that diversity is really great because it provides many perspectives and also helps reduce this bias. So how have you at LinkedIn being able to create a more diverse team? So first of all, I think it's certainly there is a, we all believe that diversity is certainly better as we're building product, thinking about if you have a diverse team that is really a representation of the customer and the members that you're serving, then you're definitely a better to be able to come. You are able to come up with better features that is able to serve the needs of the population of our member. But also at the same time, that's just the right thing to do as well, right? Thinking about, we all have had experiences that we may not feel as much belong when we walk into a room that we are the only person that we identify with to be in that room. And we certainly wanted to be able to create that environment for all the employees as well. And thinking about, I think there is also studies that has done us what makes a high performing team. Some of the studies that's done at Google with the psychological safety aspects of it, which is really, there's a lot of brain science that says when you make people feel they belong, that they will actually be so much more creative and innovative and everything, right? So we have that belief. But tactically, there are many things that we're doing from all the Dibs aspect, right? How can you bring diversity, inclusion and belonging and starting from hiring, right? So we certainly are very much emphasized on how can we increase the diversity of individuals that we're bringing to LinkedIn? And when they are LinkedIn, can we make them feel more belong? And then feel more included in every aspect. We have different inclusion groups, right? We have, I mean, obviously I'm very much involved in women in tech, at LinkedIn. We have both many efforts that we do to help women at LinkedIn in engineering and in other groups as well to feel they belong to this community at the same time. There is concrete actions that we're taking too, right? That we are helping women to have a much better understanding and aware of some of the ways that we operate that is slightly different from maybe our male colleagues would operate, right? There are certain things that we're doing to change the current processes, hiring processes, promotion process that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity. Right, that's great. And what advice would you give to women who are just starting college or who are just out of college who are interested in going into data science? So I want to say the biggest learning for me is just have that kind of attitude. I, you know, the woman biologically and all just like in every way, we're not any less than men and you certainly have seen many strong and very talented women that we have in the field. So don't let people's perception or biases around you to bring you down and then thinking about what you wanted and then just go for it and then go for the advice that you can get from people and then there are so many and we can see in the conference today, so many talented women that you can reach out to who are willing and very willing to help you as well. And in this age of AI and ML, where do you see data science going in the future? That's a really interesting question. So in the way that data science, I want to say it's a field that is really broad, right? So if you're thinking about things that I would consider to be part of data science may not necessarily part of AI, some of the causal inference that is extremely popular and important. And then there, I think the fields will continue to evolve. I think the fields will continue to evolve. There are going to be, and then the fields are continually overlapping with each other as well. You cannot do data science without understanding or have a strong skill in AI and in machine learning. And you also cannot have, you can't do great machine learning without understanding the data science either, right? So thinking about some of the talk that Daphne Kolder earlier was sharing, as in you can blindly run your algorithm and without realizing the bias that all the algorithm is really just detecting the machines that's used in the images versus actually detecting the difference between broken bones or not, right? So I think having, I do see there is a continuously big overlap. And I think the individuals who are involved in both communities should continue to be very comfortable being in that way too. Right, right. Yeah, thank you so much for being on theCUBE and thank you for your insight. Of course, thank you for having me. I'm your host, Sonia Tigare. Thank you for watching theCUBE and stay tuned for more.