 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 Tagare, and we're live at Stanford University for the fifth annual WIDS Women in Data Science Conference. Joining us today is Nung Ho, the Director of Data Science at Intuit. Nung, welcome to theCUBE. Thank you for having me here, Sonia. So tell us a little bit about your role at Intuit. Yeah, so I lead the Applied Machine Learning Teams for our QuickBooks product lines and also for our Customer Success Organization. Within my team, we do Applied Machine Learning, so we specialize in building machine learning products and delivering them into our products for our users. Great, and today you're giving a talk. You talk about how organizations want to achieve greater flexibility, speed, and cost efficiencies, and you're giving a technical vision talk today about data science in a cloud world. So what should data scientists know about data science in a cloud world? Well, I'll just give you a little bit of a preview into my talk later, because I don't want to spoil anything. Yeah, but I think one of the most important things of being a data scientist in a cloud world is that you have to fundamentally change the way you work. A lot of us start on our laptops or a server and do our work there, but when you move to the cloud, it's like all bets are off, all the limiters are off, and so how do you fully take advantage of that? How do you change your workflow? What are some of the things that are available to you that you may not know about? And in addition to that, some things that you have to rewire in your brain to operate in this new environment. And so I'm going to share some experiences that I learned firsthand and also from my team into its cloud migration over the past six years. That's great, I'm excited to hear that. And so you work it into it, Intuit has sponsored WIDs for many years now. Last year we spoke with Kavitha Sanwan from Intuit, so tell us about this Intuit sponsorship. Yeah, so Intuit, we are a champion of gender diversity and also all sorts of diversity. And when we first learned about WIDs, we said we need to be a champion of the Women in Data Science Conference because for me personally, oftentimes when I'm in a room going over technical details, I'm often the only woman. And not just that, I'm often the only woman executive. And so part of the sponsorship is to create this community of women, very technical women in this field to share our work together, to build this community, and also to show the great diversity of work that's going on across the field of data science. And so Intuit has always been really great for embracing diversity. Tell us a little bit about that experience, about being part of Intuit, and also about the tech women part. Yeah, so one of the things at Intuit that I really appreciate is we have employee groups around specific interests, and one of those employee groups is tech women at Intuit. And tech women at Intuit, the goal is to create a community of women who can provide coaching, mentorship, technical development, leadership development. And I think one of the unique things about it is that it's not just focused on the technical development side, but on helping women develop into leadership positions. For me, when I first started out, there were very few women in executive positions in our field. And data science is a brand new field. And so it takes time to get there. Now that I'm on the other side, one of the things that I want to do is be able to give back and coach the next generation. And so the tech women at Intuit group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that I can provide that coaching and that leadership development. And speaking about diversity, in the opening address we heard that diversity creates perspectives and it also takes away bias. So why is gender diversity so important to Intuit and how does it help take away that bias? Yeah, so one of the important things that I think a lot of people don't realize is when you go and you build your products, you bring in a lot of biases in how you build the product. And ultimately, the people who use your products are the general population. For us, we serve consumers, small businesses and self-employed. And if you take a look at the diversity of our customers, it mirrors the general population. And so when you think about building products, you need to bring in those diverse perspectives so you can build the best products possible because the people who are using those products come from a diverse background as well. Right. And so now at Intuit, like instead of going from a desktop-based application, we're at a cloud-based application which is a big part of your talk. How do you use data for A.B. testing and why is it important? Yeah, oh, A.B. testing. That is a personal passion of mine actually because as a scientist, what we like to do is run a lot of experiments to say, okay, what is the best thing out there? So that ultimately when you ship a new product or a feature, you send the best thing possible that's verified by data and you know exactly how users are going to react to it. When we were on desktop, it made it incredibly difficult because those were back in the days and I don't know if you remember this, but back in the days when you had a floppy disk, right? Or even a CD-ROM, that's how we shipped our products. And so all the changes that you wanted to make had to be contained in there and you really only ship it once per year. So if there's any type of testing that we did, we would bring our users in, have them use their products a little bit and then say, okay, we know exactly what we need to do, ship that out, so you only get one chance. Now that we're in the cloud, what that allows us to do is to test continuously via A.B. testing. Every new feature that comes out, we have a champion challenger model and we can say, okay, the new version that we're shipping out is this much better than the previous one? We know it performs in this way and then we get to make the decision, is this the best thing to do for our customer? And so you turn what was once a one-time process, a one-time change management process to one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers. That's awesome. So what advice could you give to the next generation of women who are interested in STEM but maybe feel like, oh, I might be the only woman, I don't know if I should do this? Yeah, I think the biggest thing for me was finding mentorship. And initially when I was very early career and even when I was doing my graduate studies, for me, a mentor was someone who was in my field. But when I first joined into it, an executive in another group who was a female said, hey, I'd like to take you aside, provide you some feedback and this is some coaching I want to give you. And that was when I realized, hey, you don't actually need to have that person be in your field to actually guide you through to the next step. And so for women who are going through their journey and are early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field they're in, because everybody has diverse perspectives and things that they can teach you as you go along. And how do you think WIDS is helping women feel like they can do data science and be a part of the community? Yeah, I think what you'll see in the program today is a huge diversity of our speakers, our panelists, through all different stages of their career and all different fields. And so what we get to see is not only the time baseline of women who are in their PhDs, all the way to very, very well-established women. The Provost of Stanford University was here today, which is amazing to see someone at the very top of their career who's been around the block. But the other thing is also the diversity in fields. When you think about data science, a lot of us think about just the tech industry, but you see it in healthcare, you see it in academia, and there's a scene that wide diversity of where data science and where women who are practicing data science come from, I think is really empowering, because you can see yourself in there and representation does matter quite a bit. Absolutely, and where do you see data science going forward? Ooh, that is a tough and interesting question actually. And I think that in the current environment today, we could talk about where it could go wrong or where it could actually open the doors. And for me, I'm an eternal optimist. And one of the things that I think is really, really exciting for the future is we're getting to a stage where we're building models, not just for the general population. We have enough data and we have enough compute where we can build a model tailored just for you for all of your likes. And for me, I think that that is really, really powerful because we can build exactly the right solution to help our customers and our users succeed. Specifically, me working in the personal and small business finance space, that means I can help that cupcake shop owner actually manage her cash flow and to help her succeed. To me, I think that's really powerful and that's where data science is headed. Nung, thank you so much for being on theCUBE and thank you for your insight. Thank you so much, Shanya. I'm Sonia Tigare, thanks for watching theCUBE. Stay tuned for more.