Polymath: This is the “jack of all trades” type of data scientist who spends his time doing all sorts of data-oriented tasks, from building platforms to gather data to analyzing data and acting on it too.spend 20% of their time doing it) fall into this category. 20% Moonlighter: Engineers and managers who only dabble in data science (i.e.Software engineers and program managers who spend half their time using data science-related skills and the other half doing something else fall into this category. 50% Moonlighter: Sometimes, you might be a data scientist but not even know it.This is a relatively small group, percentage-wise, but it was statistically significant. Insight Actor: This data science type spends nearly 60% of her time acting on insight, and nearly 20% disseminating insights from the data.They’re more likely to work with line-of-business decision makers and those in product development than the group as a whole, and less likely to work with SQL or structured data. Data Evangelist: This type of data scientists spends a good portion of her time engaging with others.Platform Builders are more likely to work in distributed systems, like Hadoop, and have “engineer” in their title, but not to have a PhD.
The first thing Kim and her colleagues discovered was that not all people practicing data science call themselves “data scientists.” Nearly 40% of the survey respondents identified as data scientists, but 24% called themselves software engineers, 18% were software engineers, while 20% had some other title. Kim and her team ran the results of the survey through a clustering algorithm (naturally) and published the results last September in a 17-page paper titled “Data Scientists in Software Teams: State of the Art and Challenges,” that can be downloaded from the IEEE Xplore Digital Library.
The research revolved around a survey of 793 professional data scientists working at Microsoft that investigated how they spent their time, what tools they use, and the challenges they face in their jobs. Miryung Kim, an associate professor in UCLA’s Computer Science Department, last week presented a session at the Strata Data Conference that showcased her research into the data science and software development community. But it turns out, not all data scientists are alike, and according to a recent analysis by researchers at UCLA and Microsoft, there are actually nine different types of data scientists. If you’ve worked with the data science community, you’ve probably interacted with data scientists and formed a definition for the increasingly popular position.