 Hey friends of theCUBE, it's Lisa Martin live at Stanford University, covering the eighth annual Women in Data Science. But you've been a CUBE fan for a long time, so you know that we've been here since the beginning of WIDS, which is 2015. We always love to come and cover this event. We learn great things about data science, about women leaders, underrepresented minorities, and this year we have a special component. We've got two grad students from Stanford's master's program and data journalism joining. One of them is here with me, Hannah Freitag. My co-host, great to have you. And we're pleased to welcome from into it for the first time, Chair Mayor Ladour Group Manager at Data Science. Sure, it's great to have you. Thank you for joining us. Thank you for having me. And I was just a secret girl talking with my boss at theCUBE who informed me that you're in great company. Into its chief technology officer, Mariana Tessel is an alumni of theCUBE. She was on at our super cloud event in January. So welcome back into it. Thank you very much. We're happy to be with you. Tell us a little bit about what you're doing. You're a data science group manager, as I mentioned, but also you've done some cool things. I want to share with the audience. You're the co-founder of the Pi Data Televive Meetups, the co-host of the unsupervised podcast about data science in Israel. You give talks about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? So I was always interested in mathematics. When I was small, I went to this special program for youth going to university. So I did my tests in mathematics earlier and studied in university some courses. And that's why I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WIDS, and Han and I and our co-host Tracy have been talking about this all day, is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity, telecom. And of course, we have this expectation that we're going to be connected 24 seven from wherever we are in the world and we can do whatever we want. I can do an Uber transaction. I can watch Netflix. I can do a bank transaction. It all is powered by data. And data science is some of the great applications of it. It's what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on it into it. I'm an Intuit user myself. But talk to me about some of those things. Give the audience really a feel for what you're doing. So if you are an Intuit product user, you probably used TurboTax in the past. I do. So for those who are not familiar, TurboTax help customers submit their taxes. Basically, my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bills, receipts, different bank statements. Yeah, so this is really exciting for me. The opportunity to work to apply data science and machine learning to a solution that actually help people. Yeah, yeah. In the past years there have been more and more digital products emerging that need some sort of data security. And how did your team or has your team developed in the past years with more and more products or companies offering digital services? Yeah, so can you clarify the question again? Sorry. Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? Well, definitely. I think, you know, since I joined Inuit I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined there were like a dozens of data scientists and machine learning engineers on Inuit and now there are a few hundreds. So we definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing that so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. I like both of those, more money in my pocket and less work. That's awesome. Exactly, exactly. Keep going into it. But one of the things that is so cool is just the abstraction of the complexity that Inuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion of euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now and so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. Definitely and I think that's what's also really amazing about Inuit. It combines human in the loop as well as AI because in some of the tax situation it's very complicated maybe to do it yourself and then there's an option to work with an expert online that goes on a video with you and helps you do your taxes and the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. And that's what it's all about is using data to be more efficient, to be faster, to be smarter but also to make complicated processes in our daily lives and our business lives just a little bit easier. One of the things I've been geeking out about recently is chat GPT. I was using it yesterday, I was telling everyone I was asking what's hot in data science and I didn't know, wouldn't know what hot is and it did, it gave me trends. But one of the things that I was so and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the CTO of open AI is a female. I didn't know that and I thought why are we not putting her on a pedestal because people are likening chat GPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not is another role model because really ultimately what WIDS is great at doing is showcasing women in technical roles because I always say you can't be what you can't see. We need to be able to see more role models, female role models, underrepresented minorities, of course men because a lot of my sponsors and mentors are men but we need more women that we can look up to and see, ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished? What inspires you to continue and be one of those females that we say, oh my God, I could be like Sheer. I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generated AI for example, there's so much that can be done to further help people make more money and do less work because there's still so much work that we do that we don't need to. This is within Intuit but also there are so many other use cases like I heard today with the talk about the police. So that was really exciting, how you can apply machine learning and data to actually help people, to help people that have been through wrongful things. So I was really moved by that and I'm also really excited about all the medical applications that we can have with data. Yeah. Yeah, it's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? Yeah, so I have so much to say on this. Good. But in my work in Tel Aviv I had the opportunity to start with Intuit Women in Data Science branch in Tel Aviv so that's why I'm super excited to be here today for that because basically this is the original conference but as you know there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018 and we've been through already, this year it's gonna be, it's next week, it's gonna be the sixth conference and every year are a number of submissions to make talk in the conference doubled itself. We started with 20 submissions, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. It's fantastic. And beyond the fact that there's so much traction I also feel the great impact it has on the community in Israel because one of the reason we started with was that when I was going to conferences I was seeing so little women on state in all the technical conferences. Kind of the reason why I guess Margot and team started the Weeds Conference so I saw the same team thing in Israel and I was always frustrated, I was organizing pay data meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough. We need more. So once we started Weeds and people saw so many examples on the stage and also females got opportunity to talk in a place that for that then it's also started spreading and you can see more and more female speakers across other conferences which are not women in data science. So I think just the fact that Intuit started this conference back in Israel and also in Bangalore and also the support Intuit does for Weeds and Stanford here. It shows how much Weeds values are aligned with our values. And I think that to show for that I think we have over 35% females in the data science and machine learning engineering roles which is pretty amazing I think comparing to the industry. Absolutely, I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that because usually if we look at the industry to your point over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org show that that number is now 27.6%. So it's very slowly. It's very slowly increasing. Going in the right direction. Too slow. And that representation of women technologists increase at every level except intern which I thought was really interesting and I wonder is there a COVID relation there? I don't know. What do we need to do to start opening up the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that hiring we've seen improvements but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural at an organization and culture can be hard to change but it sounds like you guys kind of have it dialed in. I think we definitely do. That's why I really like working in Intuit. And I think that a lot of it is with their role modeling, diversity and inclusion. And by having women leaders, when you see a woman in leadership position as a woman, it makes you want to come work at this place. And as an evidence when I build the team I started in Israel at Intuit, I have over 50% women in my team. Nice. Yeah, because when you have a woman in the interviewer's panel, it's much easier. It's more inclusive. That's why we always try to have at least one woman and also other minorities represented in our interviewer's panel. Yeah, and I think that in general, it's very important as a leader to kind of know your own biases and trying to have a defined standard and rubrics and how you evaluate people to avoid for those biases. So all of that inclusiveness in leadership really helps to get more diversity in your teams. It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time and I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that. So the biases start to be dialed down. And I think a conference like with, and it sounds like someone like you and what you've already done so far in the work that you're doing, having so many females raise their hands and want to do talks at events is a good situation, it's a good scenario and hopefully it will continue to move the needle on the percentage of females and technical roles. So we thank you, Shear, for your time sharing with us your story, what you're doing, how Intuit and WIDs are working together. It sounds like there's great alignment there. And I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. Thank you very much. I enjoyed very, very much. Good, we hope we aim to please. For our guests and for Hannah Freitag, this is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.