(upbeat music) - [Interviewer] Here, DJ, I wanna talk about the term Data Science. - Yeah. - [Interviewer} And the origin story behind that term and I know that happened at LinkedIn. So can you talk to that a little bit? - Sure, so what was happening at this point, at the time, this is maybe six to nine months before LinkedIn's ITO and what was going on is that LinkedIn data team, the Facebook data team, were really starting to show that interesting things could happen.
There was a product that was put out by LinkedIn that was, you can view your entire network graph, it was called LinkedIn InMaps, and people were just blown away by like, hey, there's these clusters of how, my network might be connected. Facebook was starting to do really clever things with showing you new types of content and other type things. And there was this view of... Well, who are these people? And, as we're getting ready to the IPO, one of the things that was happening to me was, the HR was coming back and saying, by the way, you have a lot of job titles on the team.
You have Analyst, Business Analyst, Research Scientist, Analytics Research Scientist, and so on, and so on, we were like, oh, we just, basically always let people call themselves whatever they wanted to. Then they said, well, you know, there's engineers and there's designers and so we should figure out what to call ourselves. At the same time, Jeff Hammerbacher, who is running the data team at Facebook, he had the same problem. His HR team was also saying the same thing, and so we kinda went through the list. And you're like, Analyst, hmm, like when you hear the word Analyst, what do you think? You kinda think oftentimes, oh, well, maybe that's a management consultant type person or somebody on Wall Street.
Research Analyst, definitely more that. Research Scientist? Do you think more academia, typically, more a person who's going to focus on kind of a longer horizon type problem? Like what do you call ourselves? What do we call... And so, we went through the list, the last item on the list was, Jeff was like, well, Data Scientist. We kinda do science, but we do it with data, but Monica Rogati, who is a Data Scientist at LinkedIn, she had the idea, said, well, but we're LinkedIn, we own all the job postings, so why don't we just post all the jobs with each of the job titles and see who applies.
So we did. We saw that everyone applied to Data Scientist. We're like, hey, guess what? We're Data Scientists. So we Data Scienced our way into the term Data Scientist. The reason, I think, it fundamentally has taken off is because no one knows what it means, and that may sound kinda weird, but think about it for a second. You're in a giant meeting and you're the Data Scientist and someone sits next to you and if you're titled Analyst or Business Intelligence, and the person says, hey, so what role are you in? And you say, well, I'm the Business Intelligence person.
They'd be like, why are you here? Now, if you're the Data Scientist, they're like, hey, so why are you here? And you're like, well, I'm the Data Scientist. They're like (clicks tongue), good, you're the smart person. (chuckles) Glad we got you here. So why the disconnect? Because we've put an incredibly well defined box around the role of what we traditionally thought people with technology and data could do. And the Data Scientist one allows you into the room and then, once you're in the room, that person that has ability of like, well, what could we do with this? What could we do...
Maybe we could do something interesting this way? And, as a result, new things come out of that and the new paradigms are created. So then you see the force multiplier of how data is used, and, as a result of the people, the broad set of people who've been in those roles and have been allowed into those conversations, they have over-delivered in the value proposition of what can happen and, as a result, that term has just taken off. (upbeat music)
Skill Level Intermediate
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