>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. >> Hi, I'm Lisa Martin, welcome back to theCUBE. We are live at Stanford University at the second annual Women in Data Science Conference, the one-day tech conference and we are joined by Julie Yoo, who is the founder and chief data scientist of Pymetrics. Julie, you were on the customer panel today. So welcome to theCUBE. >> Thank you. >> It's great to have you, it's such an interesting background. >> Julie: Thank you. >> Neuroscience meets engineering, or engineering meets neuroscience. I'd love for us to understand a little bit more about those two, how they're combined, and also, about Pymetrics. But give us a little bit of a background, as a woman in the sciences, how you got to where you are now. >> As you mentioned, my background's in computer engineering and I went into PhD program in electrical and computer engineering 'cause I wanted to study artificial intelligence. I was fascinated by the notion of artificial intelligence. So my research topic started in automatic speech recognition systems, building computers to decode and decipher human speech. After a couple of years, I got frustrated with just the engineering approach or statistical methods-based approach to improving the existing speech recognition systems that are out there, 'cause I thought to myself, We're trying to make computers understand human speech and mimic human function when we don't really understand how our brain works and I don't really know exactly what happens when you listen to you speak, when I listen to you speak and when you listen to I speak, what is going on? We didn't really have a good sense, so I wanted to study neuroscience. So I quit engineering and I went into PhD program in neuroscience and there, I started doing a lot of neuroimaging study, just looking at human cognition and just figuring out what is going on when people perceive and process these signals that are out there. >> And was your idea to eventually marry the two? >> I didn't really think about it that way, but it just sort of happened, as in like, my background in engineering sort of homed me into doing some of the projects that I did when I was doing my PhD and my post-doc. And while I was doing all that, I just evolved to be a data scientist without, really, me realizing I was doing everything that a typical data scientist would do. And this was even before 2008. The job title of data scientist wasn't even around then, so it sort of happened because of where I came from and because what I was interested in and as I was doing that, it just ended up being a good marriage. >> And there it was. Talk to us, tell people what Pymetrics is and what the genesis of this company was. >> Pymetrics is a platform that uses neuroscience-based games and data science to promote predictive and bias-free hiring. How we became a product was because I was going through post-doc and my co-founder was also going through business school and we were both going through the phase of, Okay, we don't want to stay in academia. What do we want to do with our lives? And at the time, we realized a lot of the career-advising tools that are out there were not scientific and they were not data-driven and we felt that there is a clear need for a tool that can actually use all these data that are out there to help people figure out what they should be doing with their lives. So we thought we were uniquely positioned to use our background in engineering and neuroscience and build a product that could actually solve these challenging problem and that's how we started Pymetrics. >> That's fantastic. You started about three years ago in 2013. So, really getting rid of some of the biases, share with us what some of the biases are. Is it test scores, SATs, MCATs, GPAs? >> There are many, many different kinds of biases in hiring process right now, I think. There is a preconception of what an engineer should look like and I think that plays a lot. And when you do going to an interview, how you look and how you dress, it adds to the bias. There is ethnic bias, there's gender bias, and there is bias based on test scores and what school you went to. So we want to remove ourselves from that and really get down to what kind of person you are and are you really... I guess, have the right set of skills to succeed in certain job functions. We do that by measuring, instead of taking your subjective answers from questionnaires, we do that by objectively measuring your behavior and these games are based on neuroscience research so we know that they actually measure things that we want them to measure, for instance, your ability to pay attention, your risk appetite, and all those things that we think matters as to what makes you good at certain things and not so good at some other things. So we use these objective data and data science and predictive modeling to come up with predictions as to how good you will be in certain career versus some other career. >> Really, an incredible need for that. It's game-based, so it's an actual game that people will play that will help understand more of who they are as a person, their behaviors, those patterns. Tell us a little bit about the invention of the game, what was it like, who was it for? >> The games were actually sourced from neuroscience research community. We did not create these games. What we did was we actually just took them from research and medical settings and applied it through hiring. We know that these are relevant to measuring your attributes and your personality, so why not use it for hiring and career advising, because it makes sense. We're trying to measure your qualities, your soft skills and what-not, why not just use it for something that could really benefit from these sort of data. What we did do is we actually made these games, they're not really called games in research community, but we made it shorter and we made it more applicable to the things that we are trying to use if for. >> You took feedback from some of your earlier adopters who were saying maybe it's taking me too long, maybe some of the recruiters might say, they gave you some very viable feedback that have helped you optimize the products. >> Right, as a data scientist, I always think the more data, the better, but that also means that people would have to sit in front of their computers and play an hour-long battery of games and a lot of people were thinking that it might be just a tad too long and companies felt that spending 45 minutes to an hour could be a discouraging thing and people felt fatigue effect and we could see that in the results, so we ended up making it shorter. We went from 20 games to 12 games and we cut it down to 25 minutes long and I think, now, we're in the sweet spot where we do get enough data but, at the same time, we're not making it an hour long. >> Right, so this is really targeted for people coming out of university programs, whether it's bachelor's, master's, doctorate, et cetera, and also, what type of companies who are looking to hire, what's kind of your target market for that? >> I think mostly Fortune 500 companies 'cause a lot of these companies do hire in large volume, so it helps to have us go to these companies and build their models based off of their employees. And if a smaller company comes along and they only have 10 employees in the job function, then it's extremely difficult for us to build the model base off of their 10 employees, whereas if it's a larger corporation, then we can have 200 employees play and we can build the model based on their data. So generally, large corporations is our target clients. >> I'm curious, in terms of some of the data that you are seeing, that you're analyzing, are you seeing, we look at data science as a great example of the event that we're at, in report from Forbes recently that said it's the best job to apply for in 2017. We're looking at now what's going to be happening, predicted over the course of the next year, and that's a shortage in talent. Are you seeing, with some of the data that you're taking in, are you seeing things that are mapping to that, like people that are really geared towards that? Or are you seeing more companies that are looking for computer-industry, data-science type roles? Is that increasing, as well? >> I think companies are definitely looking for more data scientists and I think, also, people are figuring out that there are data science programs like graduate school programs and I think that supply of data scientists is definitely increasing, but at the same time, or more so, the demand for data scientists is increasing. And not to mention, the available data that's out there is increasing at a faster rate than anything else. Yeah, it is, I think, the best time to be a data scientist right now. >> Let me ask you one more question about looking at skills. We have such a great cross-section at this event of leaders in retail, in obviously, what you're doing in neuroscience-gaming-merging world. We've got professors here. Data science is such an interesting topic, it's obviously very horizontal. From a skill set perspective, kind of the traditional skills of being a statistician, mathematics, being a hacker, a lot of the things that we've been hearing around the show today, and really aligns with what you're doing is more on the behavioral insight side of, you have to be able to communicate what you're seeing and be able to apply it. I'd love to understand a profile of an ideal data scientist that you guys are seeing from your data. What are some of the other behavioral attributes that maybe are some of the non-teachable things that you're seeing that really come up that this would be a great career path for someone? >> Personally, I think intellectual curiosity is number one, and they would have to have strong self-motivation and discipline because you could love analyzing data and you could just be doing that for how many days, I don't know, and that's it. You could actually come up with a good story. You've got to be a good storyteller and if you have artistic flair to make the data beautiful, then even better. But it is important to go from the beginning of the project where you have a bunch of data set and actually come up with actionable results that people can use. And you're not only always going to be communicating with a data scientist, so you need to be able to present your data in a more succinct and easily-digestible way. >> That sounds like, as the chief data scientist for Pymetrics, that's what you're looking for to hire on your team. Give us a little bit, last question here, just a little bit of an overview of what your data science team looks like at Pymetrics, as you're helping to leverage this data to give people opportunities with careers. What does your team look like? >> Our team has a very diverse background. We have a few PhD's in Physics and you know, well, I have a PhD in Neuroscience and there's other data scientists with PhD's in Physics. We actually have one guy who majored in Data Science and we have another guy who majored in Bio Engineering. So it's definitely a diverse background. But the general theme is that you do need a good, quantitative foundation. So, whether it's engineering or physics, it is still helpful to have that statistical or analytical mind and if you can actually apply that, and actually love solving problems then I think data scientist is a right goal. >> So you're on the career panel at WiDS2017, is that the advice that you would give to kind of, the next generation of kids that are interested in this but aren't quite sure what industry they would want to go into? >> What industry? I think, I mean if they're even remotely interested in going into data science, I would encourage them to pursue it. I think it is one of the most fascinating fields right now and there's never going to be a shortage of needs for data scientists. So if you like it, if you think you are going to be pretty good at it, I say go for it. >> Fantastic. And you've got a great audience here. This is being live streamed in 20 cities, I think across the globe, or 75 cities, I have to get those stats right. But, there's a big opportunity here to be an influencer and we thank you for spending some time with us. Best of luck on the panel. >> Thank you. >> Thank you for watching. I'm Lisa Martin, we are live with theCUBE, at Women and Data Science 2017, #WiDS2017. Stick around, we'll be right back. (upbeat mellow music)