>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. Yeah. >>Hi. And welcome to the Cube. I'm your host Sonia Category. And we're live at Stanford University for the fifth annual Woods Women in Data Science Conference. Joining us today is none. Ho, the director of data Science at Intuit None. Welcome to the Cube. >>Thank you for having me here, so yeah, >>so tell us a little bit about your role at Intuit. So I leave 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 what? We specialize in building machine learning products and delivering them into our products for >>our users. Great. Today. Today you're giving a talk. You talked about how organizations want to achieve greater flexibility, speed and cost efficiencies on. And you're giving it a technical vision. Talk today about data science in the 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 being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of a start on our laptops or a server and do our work. 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 some things that you have to rewire in your brain to operate in this new environment. And I'm going to share some experiences that I learned firsthand and also from my team in into its cloud migration over the past six years. >>That's great. Excited to hear that on DSO you were getting into it into it has sponsored Woods for many years now. Last year we spoke with could be the San Juan from Intuit. So tell us about this Intuit's sponsorship. Yeah, >>so into it. We are a champion of gender diversity and also all sorts of diversity. And when we first learned about which we said, We need to be a champion of the women in data science conference because for me personally, often times when I'm in a room, um, going over technical details I'm often the only woman and not just 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 about bad experience, about being part of Intuit and also about the tech women part. Yeah, >>so one of the things that into it that I really appreciate is we have employees groups around specific interests, and one of those employees 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 in that leadership development >>and speaking about like diversity. In the opening address, we heard that diversity creates perspectives, and it also takes away bias. So why gender diversity is so important into it, 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 and how you build the product and ultimately the people who use your products are the general population for us. We serve consumer, 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 could build the best products possible because of 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 Teoh for a B testing and why is it important? >>Yeah, 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 and say, Okay, what is the best thing out there so that ultimately, when you ship a new product or 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, they made it incredibly difficult because those were back in the days. And I don't know if you remember those put back in the days when you had a floppy disk, right or even a CD ROM's. That's how we shipped our products. And so all the changes that you wanted to make had to be contained. In the end, you really only ship it once per year. So if there's any type of testing that we did, we're bringing our users and have them use our products a little bit and then say Okay, we know exactly what we need to dio 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 got to make the decision. Is this the best thing to do for a customer? And so you turn what was once a one time process, a one time change management process. So 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. Um, so, um, what advice would 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 that the biggest >>thing for me was finding men's ownership, and initially, when I was very early career and even when I was doing my graduate studies for me, a mentor with someone who was in my field. But when I first joined into it, an executive in another group who is a female, said, Hey, I'd like to take your side, provide you some feedback, and this is some coaching I want to give you, And that was when I realized you don't actually need to have that person be in your field to actually guide you through to the next up. And so, for women who are going through their journey and early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field there in, because everybody has diverse perspectives and things that they can teach you as you go along. >>And how do you think Woods 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 the career who's been around the block. But the other thing is also the diversity and 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 it's really empowering because you can see yourself in the representation does matter quite a bit. >>Absolutely. And where do you see data science going forward? >>Oh, that is a, uh, 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 on 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. Taylor just for you, for all of your life's on 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 friend, Small business finance lease. That means I can hope that cupcake shop owner actually manage her cash flow and help her succeed to me that I think that's really powerful. And that's where data science is headed. >>None. Thank you so much for being on the Cube and thank you for your insight. Thank you so much. I'm so sorry. Thanks for watching the Cube. Stay tuned for more. Yeah, Yeah, yeah, yeah, yeah, yeah.