 from Stanford University in Palo Alto, California. It's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. Back to theCUBE, our continuing coverage of Women in Data Science 2018 continues. I'm Lisa Martin. Live from Stanford University, we have had a great array of guests this morning from speakers, panelists, as well as attendees. This is an incredible one-day technical event, and we're very excited to be joined by one of the panelists on the career panel this afternoon, Dr. Jennifer Prenki, the head of Data Science at Atlassian. Welcome to theCUBE. Hi, it's my pleasure to be here. It's exciting to have you here. So you lead all sorts of machine learning initiatives at Atlassian, but you were telling me something that's interesting about your team. Tell us about that. Yeah, so the interesting thing about my team is even though I'm the head of Data Science, my team is not 100% data scientists, so the belief of the company is that we really wanted to be in charge of our own destiny and be able to deploy our models ourselves and not be dependent on other people to make deployment faster. Was that one of the interesting kind of culture elements that attracted you last year to Atlassian? No, so what is really interesting about Atlassian is it's definitely a company that creates products that I would say virtually every single software company in the world is using. And they have a very strong software engineering culture and so last year they decided to embrace Data Science and so I thought it was a very interesting challenge for me to try and infuse a little bit of my passion for data and data-drivenness to the company. So you had quite a fast ramp at Atlassian. You joined last summer and in less than six months you grew your team of data scientists and engineers from three people to 15, and it gets better, in less than six months across three locations, Mountain View, San Francisco, and Sydney. How, what were some of the key things for you that led you to make that impact so quickly? I think most data scientists on the world are interested in making an impact and this is a company that obviously does a lot of impact and a lot of people talk about this company and there is obviously a lot of interesting data and so I think one of the amazing things is that we have a very important role to play, right? Because we are in a position where we have data related to the way people work with each other, collaborate with each other, and this is a very unique data set so it's usually really easy to attract people to Atlassian. You mentioned collaboration and that's certainly an undertone here at Woods. I mentioned it's third year. You were here last year as an attendee, now you're here this year as a speaker. They've grown this event dramatically in a couple of years alone. The opportunity to reach, you know, they're expecting 100,000 to engage. It's 177 regional events. Marco Gerritzen gave us that number about an hour ago in 53 countries. What is it about with the attractive you, not only back this year, but to welcome the opportunity to be on this career panel? I actually tell you something. So, you know, like we talk about diversity and I think people usually think of diversity as, you know, like meeting some kind of free show, some bar, right? I mean to have like equality between male and female or specific minorities. I think people tend to forget that the real diversity is diversity of thought. And so I actually found out that the very first data science job I actually got, I was actually the only person who had a background in applied math and everybody else was coming from background in computer science and I quickly realized that, you know, like I'm the only person who's really like trying to push for let's validate our models really properly, et cetera. And so that made me realize how important that is to have like a lot of diversity. And so I think Woods is definitely a place where you see lots of women interested in the same thing, but coming from different perspective, different horizons at different levels of the other career. And this is really something really unique in the industry. Diversity of thought, I love that. I've not heard that before. I'm going to use that, but I'll give you credit for it. But that is one of the things that is so, the more people that we speak to, not just at Woods, but at events like this on theCUBE, is you hear there's still such a need, obviously, the scale at which the Woods has grown shows clear demand for we need more awareness that this diversity is missing. But in the fact that data science is so, it's almost horizontal, if you will, across every industry. And it sort of is blurring the boundaries between rigid job roles, doctor, lawyer, attorney, teacher, whatever. This is really quite pervasive and it provides the opportunity for data scientists globally to be able to make massive impact. But also it's still, as Margo Garrison was sharing earlier, it still requires what you said is that diversity in thought because having a particular small set of perspectives evaluating data, you think about it from an enterprise perspective, the types of companies that Atlassian deals with, and they are looking to grow and expand and launch new business models. But if the thought diversity is narrow, there's probably a lot of opportunity that it's never going to be discovered. That's right. So one of the things also I found interesting in your background was that you found yourself sort of at this interesting juxtaposition of being a mentor and going, wait a minute, this now gives you a great opportunity, but it also comes with some overhead, right? You've got it from a management perspective. What is that sort of crossroads that you found yourself reaching and what have you done with that? No, so I think, you know, like, it's probably every single technical role, but maybe data science more than others. You know, like, you have to be technical to be part of the story, right? I mean, so I think people need to have a leader that they can relate to, and I think it's very important that you're still part of this. And it's particularly interesting for data science because data science is a field that moves so quickly, right? I mean, so usually you have people moving on to, you know, like data science manager positions after being an IC. And so if you don't make a conscious effort to remain that, you know, like technical point of contact person that people trust and people go to, then, you know, like, when I think back of the technologies that were, you know, like trendy when I was still an IC compared to now, like it's really important for the managers to be still aware of that in order to do a good job as a mentor and as a leader. You also said something, I think, before we went live, that is an important element for the women that Woods is aiming to inspire and educate today, those that are new into the field or are thinking about it, as well as those who've been it for a while. But it's, there is not just getting there and going, yes, I'm interested, this is my passion, I want to have a career in this. It's also having to learn how to be a female leader. And as you mentioned from a management perspective, you got to learn, you have to know how to be assertive. Tell us a little bit about some of the trails and tribulations that you have encountered in that respect. No, that's a very interesting question because I'm actually very happy to see that nowadays I think it's becoming easier and easier for women to step into individual contributor positions because I think that people realize now that a woman can do just as good as a job as a man for a defined position. But when you're actually in a leadership position, you have to step into a thought leadership role. And so basically, you sometimes have to be in a meeting where you only have other male engineers or male leader scientists over there and say like, you know what, I disagree with you, right? And so this as a woman becomes a little bit challenging because following the processes that are already in place, I believe that people have realized that it's okay for a woman to do that. But then being the assertive person that goes against the flow and says, you are not thinking about it the right way, might sometimes be a problem because women are not being perceived as creatures that are naturally assertive, right? And so it's typical for people like a head of data science, female head data scientist to be in a situation where they're perceived as being maybe a little bit aggressive or a little bit pushy. And you know, like you'll sometimes fall into this, you're like old saying like, he's the boss, she's bossy kind of thing. And that is a challenge. I had someone once tell me a couple of years ago, and I'm in tech as well, that I was pushy. And it was, I think this was a language barrier thing. I think he meant to say persistent. But on that front, tell me a little bit more about your team of data scientists and engineers and the females on your team. How do you help coach them to embrace, it's okay to speak your mind? What's that like been like for you? So I would say like, I was actually pretty soft spoken myself. And so at some point I realized that public speaking actually helped pick up there. So I mean, somebody at some point like told me like you should go and you're brilliant technically, like go speak at a conference. And then I realized like people are listening to me actually like you always have a little bit of like imposter syndrome kind of problem as a woman. So it helped me overcome this. And so now I'm kind of trying to stimulate the ladies on my group to do the same thing because that has worked really well for me I think. You have to get outside your conference zone and try to things that help you have the self-confidence in order for you to get to the level of certainness you need to become successful. Exactly, we've had a number of women on the show today alone talk about getting outside of your comfort zone. And one of my mentors always says get comfortably uncomfortable. And that's not an easy thing to achieve. But I think you walk in the door at widths and you instantly feel inspired and empowered. And I think a number of the women that we've had on today already have talked about having sort of being charged as a mentor with the responsibility like you just said of helping those that are following your footsteps maybe understand how to have that confidence and then have that kind of that right balance. So there's, you know, there's professionals in there there's respect, but it's not just about getting them into the field it's about teaching them how to once you're there how to navigate a career path that is successful. But that's an interesting thought because I actually believe that, you know, like getting comfortable with the uncomfortable is definitely something that data science is about, right? Because you have new technologies, you have new models, you have like lateral moves, like I actually was in the advertising industry as a data scientist before switching to e-commerce and then eventually to the software industry. So, you know, like I think that people who are trained to be data scientists are like like that. And so they should also be comfortable to with the uncomfortable in their daily lives. Yeah. So you're mentioning before we went on that some of the people that you work with are like it's my hope and dream to be at Woods next year. What are some of the things that you've heard as we're at the halfway mark of Woods today that you're going to go back and share with your team as well as maybe your friends, other females that are working in STEM fields as well. I would say, you know, like last year I was here like just listening to other people or whatever this year I'm on the panel. So I mean, I'm just like, you know, like nothing is impossible. I think like, I mean, we've proven that over and over again in data science, right? I mean, who would have thought that 10 years ago like we would be at that level of understanding of artificial intelligence and the entire field, right? I mean, so it's just, yeah, I mean, it's all about like waiting and see what the future has to bring to you and we have all these amazing women today actually show us that it's possible to get there and it's exciting to be. It is, it's possible and it's exciting. Well, Jennifer, thanks so much for carving out some of your time today to speak with us. We wish you continued success at Atlassian and we look forward to seeing you back at WIDS next year. Thank you. We want to thank you for watching theCUBE. We're live at Stanford University at the third annual Women in Data Science Conference. Hashtag WIDS 2018, join the conversation. I'll be right back with my next guest after a short break.