>> Announcer: Live, from Stanford University, it's the Cube, covering The Women in Data Science Conference, 2017. >> Welcome back to The Cube, we are live at Stanford University, at the 2nd annual Women in Data Science Conference, this great, fantastic one day technical conference. And we are so excited to be joined by Yael Garten, who was one of the career panelists. Yael, you are the Director of Data Science at LinkedIn, welcome to the cube. >> Yeah, thank you, thanks for having me. So excited to have you here, everybody knows LinkedIn. My parents even have probably multiple LinkedIn accounts, but they do. You've served, what 400 and plus million accounts, I'd love to understand, what is the role, what's the data scientist's role in the business overall? >> Yeah, so I guess when people ask me about data science, what I love to kind of start with is there are a couple different types of data science. And so I would basically say that there are two main categories by which we use data science at LinkedIn. If you think about it, there is really data science where a product of your work is for a human to consume. So using data to help inform business or product strategy, to make better products, make more informed decisions about how you're investing your resources. So that's one side, which is often called decision sciences, or advanced analytics. Another type of data science is where the consumer of the output is a machine. Alright so rather than a human, a machine. So basically they these are things like machine learning models and recommendation systems. So we have really both of those. The second category is what we call data products. And so we use those in virtually everything we do. So on the data products, much of LinkedIn is a data product, it's really based on date. Right, our profiles, our connection graph, the way that people are engaging with LinkedIn helps us improve the product for our members and clients. And then we use that data internally, to really make better decisions, to understand, you know how can we better serve the world's professionals, and make them more productive and successful? >> Right, fantastic, so tell us a little bit about your team. It sounds like it's sort of broken into those two domains. You must have quite a, a large team, or a lean team? >> So yeah, we have, the way we have our team is that we work really closely within all of our product verticals, and we embed closely with the business, to really understand kind of what are the needs. And then we work very cross-functionally. So we will typically have in any group, sort of a product manager, and engineer, a designer, a data scientist, often it's from both kinds of data scientists. So sort of one on the analytic side, one on the machine learning side. Right, marketing, business operation, so really very cross-functional teams working together, using this data. >> Very smart, it sounds very integrated from the beginning, where they kind of by design-- >> Yes. >> So that collaboration is really sort of natural within LinkedIn? >> Yes. >> That's fantastic, very progressive. And certainly it's something that everybody benefits from. >> Yes. >> Right because as whether you're on the advanced analytic side, or on the machine learning side, you're getting exposure to the business side, vice versa, which, that's really a great environment for success. >> Yes, yeah and part of, I think, what I love about LinkedIn is actually our data culture, and how kind of data is infused in the culture of how we do things. >> Right, which is really-- >> Right, not always the case. >> It's not, and it's, cultural shifts have, we were talking about that with a number of guests today, and especially the size of the organization, that's tough. >> Yael: Yes. >> So to have that built in and that integration as part of, this is how we do business is, really you can imagine all the potential and possibilities there. So would love to understand, how is LinkedIn using data to recommend ways to evolve products and services to best serve all of it's members? >> Yeah, so maybe two different examples of how we do this, one is, what we do is every launch that we have, so every feature that we generate, we really do it at an online experimentation setting. So we have a certain feature that we're about to roll out to our members. And we want to make sure that it's a better experience for our members. And better, as measured by kind of the metrics that we've defined in terms of measures of success. And so, which is really aligned to what value we believe we're delivering our members and customers. And so when we roll out features, we'll roll it out to a certain percentage of our users, test the downstream impacts of that, and then decide, based on that, whether we actually roll that feature out to 100% of members. And so that's one of the things that my team is heavily involved in, is really helping to use that data to make sure that we are structuring things in a way that's statistically sound, so that we can measure the impacts correctly, of rolling out certain features. So that's kind of one category of work. And the other category is really to, to do sort of opportunity identification, and kind of deep-dive insights into understanding into a certain product area. Where are there opportunities to improve the product? So one, let me give you a high-level example. One of the ways we might use data is to say okay, Are certain members in certain countries accessing via iOS or Android? And if so, should we be developing more in differentiating between iOS and Android apps? It's one simple example right, where we'll actually decide our R&D investments, based on the data that we're seeing in terms of how people are using our products and do we think that that's important enough of an investment to improve the products and invest in that area? >> Wow very, very smart. What are some of the basic ways that data scientists can deliver more value for their stakeholders, whether they're internal stakeholders, across different functions within the organization, or the members, the external stakeholders? >> Yeah, I think one of the most important things is to really embed closely into these kind of functional or domain areas, and understand qualitatively and quantitatively, what's important. Right, so understanding what the business context is and what problem you're trying to solve. And I think one of the most important that data scientists play a role is actually helping to ensure are we even answering the right question? So as an example, a product manager might ask a data scientist to pull certain data, or to do a certain analysis, and a part of the conversation and the culture has to be what are you trying to get at? What are you trying to understand? And really thinking through is that even the right question to be asking? Or could we ask it in a different way? Because that's going to inform what analysis you do, right what, really what, how you're delivering the results of this analysis to make better decisions. So I think that's a big part of it is, having this iterative process of doing data science. >> Really, it sounds like such and innovative culture, and you're right, looking at the data to determine is this the right next step? Is it not? How do we maybe adapt and change based on really what this data is telling us. If we kind of look at collaboration for a second. You talked about the integrated teams, but I'm wondering how do you scale collaboration within LinkedIn across so many businesses and engineering stakeholders? >> Yeah, so the way I kind of like to think about it is, there's really, you have to invest in culture, process, and tools. So let me start from the bottom up. So on the tools or technology, one of the ways to do it, is actually to create self-served tools, to really democratize the data. So first of all investing in foundations of really good data quality, right, whether you're creating that data yourself, or you're collecting that from externally, from different organizations. Once you have really good data quality, making sure that you have foundations that enable self-serve data basically. So for example, some of the things that data scientists are used today in various companies, really doesn't need a data scientist if you've invested in ways where business partners, let's say, can quarry that data themselves. So they don't need a data scientist to be doing this role. So that's an important investment on the technology side. In addition, making data scientists really productive, by using and investing in tools that will enable them to access the data is really important. So once you have that sort of technology, it enables your data scientist to be productive. The process is really important. So just as an example we have a sort of playbook in terms of how do we launch features? And part of that is kind of bring in data insights, in terms of which features we should be building. And then once you've determined how using the data on those insights, it's okay how are we going to launch this in terms of experimental design and setting? And then what are the success metrics? How are we going to know that this actually a good-- (speaker drowned out by crashing sound) And then once we've launched the experiment, analyzing that, where all of the stakeholders are part of this right? The project manager, the executive, the engineer, the data scientist, and then kind of iterating on the results and deciding what the decision is. So having actually a process that the whole team or the company abides by, really helps at having this collaboration where it's clear what everyone is doing and kind of what's the process by which we use data to develop and to innovate? And then finally culture, I think that's such an important part, and that really needs to be sort of bottoms up, top down, everywhere. It really needs to be a community and a culture where data is discussed and where data is expected, and where decision making really is grounded on, on data. I fundamentally believe that any product being developed, or any decision being made really should be data informed if not data driven. >> Right absolutely. One of the things that I'm hearing in what you're doing is enabling some of business users to be self-sufficient. So you're taking that feedback and that input from the business side to be able to determine what tools they need to have and how you need to enable them so that you've got your resources aligned on certain products. >> Yeah, just as an example, one of the things that we do for example, is we realized over time that, this isn't actually productive, and how do we make ourselves scale, so we started doing data boot camps, for example. >> Interviewer: Okay. >> Where we'll actually train new people coming into the company, on data, and on self-serve tools, and on how to run experiments. And so a variety of different kind of aspects, and even how to work with data scientists productively. So we have actually train that >> fantastic. >> So this data boot camp really helps us to instill a data culture, and it rally empowers the team. >> So this is, anybody coming in, whether they're coming in for a marketing role, or a sales ops role, they get this data boot camp? >> Yeah. >> Wow. >> And it's open to anyone and you know, it yeah, typically is going to be a certain subset of those people, but it really is open to anyone, and we're talking about more ways of how do we scale that and maybe how we put that on LinkedIn learning and make that more broadly accessible. >> Yeah. >> Yeah. >> So you have quite a big team, how do you keep all of the data scientists that you've got happy, what are the challenges that they face, how do you evaluate those challenges and move forward so that they have an opportunity to make an impact at LinkedIn? >> Yeah, so part of the things are actually the things that I mentioned right? So a culture of data so a, it's really important when we see that this is not happening, actually addressing that. So data scientists are going to thrive in a community where data is valued, and where data scientists are valued, so that's actually a really important aspect. And you know luckily people come to use because they know that we do value data. But I think that that's very important for any company and so, I advise startups as well, and this is one of the things that I tell people that are founding companies, is you have to have a culture which values data to attract data scientists, because otherwise they have other options. The other thing is having these, these foundations that enable them to be productive. Right, so these tools and these systems that enable them to really do high-value work, and invest in the right areas. So start graduating from doing things that are more, maybe repetitive or low-level and figure out how do you scale that so that you can have data scientists really, efficiently using their time for things that only they can do? >> Right, I love that this culture is sort of grooming them. One of the things that, a couple things I read recently. One, was that, I think it was Forbes that said, 2017, the best job to apply for is data scientist. But, from an trends perspective, it's looking that by 2018, there's going to be a demand so high, there's not going to be enough talent. How are, what's your perspective on LinkedIn? Are you, have you, it sounds like from a foundational perspective, it is a data driven company that really values data, is that something that you see as a potential issue or you really have built a culture of such, not just collaboration and innovation, but education that LinkedIn is in a very good position? >> Yeah, well so one thing is that, I didn't mention in terms of the happiness factor right? Is that it is actually a place where data scientists look for a place where they can also grow and learn and be with other like-minded data scientists. So I think that's something that we strongly support, again for companies that, people that may be viewing this and are not in such environments, there are a lot of ways to do this. So keeping data scientists happy also can be facilitating meetups, right with data scientists from your local region, and so those are ways that people share information and share techniques and share challenges even right? >> Interviewer: Yeah. >> Because this a growing and evolving field. And so that's, having that community and one of the things that's amazing about this conference is that it's creating this community of data scientists that are all sharing successes and failures as data science is evolving. The other thing is that data science draws from so many different backgrounds right? >> Yeah. >> It's a broad field, right, and there's so many different kinds of data science, and even that is getting both more specialized and more broad. So I think that part of it is also looking at different backgrounds, different educational backgrounds and figuring out how can you expand the pool of people that you're looking at, you know that are data scientists? >> Interviewer: Right. >> And how do you augment what skills they may not have yet, you know, on the job or through training or through online education, and so we're looking at all of these ways so. >> That's fantastic, we've heard a lot of that today. The fact that, the core data science skills are still absolutely vital, but there's some other sort of softer skills, you talked about sharing. Communication has come up a number of times today. It's really a key, not only to be able to understand and interpret the data from a creative perspective and communicate what the data say. But to your point, to grow and learn and keep the data scientists happy, that social skill element is quite important. >> Yael: Yes. >> So that was, that was an interesting learning that I heard today, and I'm sure you've heard many interesting things today that have inspired you as well. >> Yeah, and that's something that you know, creating this culture is something that even data science leaders around the world, where we're discussing this and talking about this, you know what are the challenges? And how do we evolve this field? And how do we help define and help kind of groom the next generation of data scientists? >> Interviewer: Right. >> And to be in a more stable and be in a better place than where we were and to help to continue to evolve it, and so it is yeah. >> Evolution, it's a great word. I think that that's another theme that we've heard today and as much as I'm sure you've inspired and educated these women that are here. Not just in person today, but all the what 70, 70 cities and 25 countries it's being live streamed. >> Yael: Yeah, it was 80 cities and six continets. >> It's growing it's amazing. >> And yeah. >> And I'm sure that they'd vote a 10 from you, but it's probably just in the little bit that we've had a time to chat, I'm sure that you're probably gleaning a lot from them as well. >> Yeah, definitely, absolutely. >> And it's the, we're scratching the surface. >> Yes, absolutely and so there are many more years to come. >> Interviewer: Exactly, Yeal thank you so much for joining us on The Cube. >> Thank you, it's pleasure. >> It's a pleasure talking to you, we wish you continued success at LinkedIn. >> Thank you, it's a pleasure. >> And we want to thank you for watching The Cube. We've had a great day at the 2nd annual Women in Data Science conference at Stanford University. Join the conversation #wids2017. Thanks so much for watching, we'll see ya next time. (rhythmic music) >> Voiceover: Yeah.