Roger Magoulas - 2015 Data Science Salary Survey

FOR THE THIRD YEAR RUNNING, we at OReilly Media have collected survey data from data scientists, engineers, and others in the data space about their skills, tools, and salary. Some of the same patterns we saw last year are still presentnewer, scalable open source tools in general correlate with higher salaries, Spark in particular continues to establish itself as a top tool. Much of this is apparent from other sources: large software companies that traditionally produced only proprietary software have begun to embrace open source; Spark courses, training programs, and conference talks have sprung up in great numbers. But who actually uses which tools (and are the old ones really disappearing)? Which tools do the highest earners use, and is it fair to attribute a particular variation in salary to using a certain tool? We hope that the findings in this iteration of the Data Science Salary Survey will go beyond what is already obvious to any data scientist or Strata attendee.

This report is based on an online survey open from November 2014 to July 2015, publicized to the OReilly audience but open to anyone who had the link. Of the 820 respondents who answered at least one question, about a quarter dropped out before completing the survey and have been excluded from all segments of analysis except for those showing responses to single questions. We should be careful when making conclusions about survey data from a self-selecting sampleit is a major assumption to claim it is an unbiased representation of all data scientists and engineersbut with a little knowledge about our audience, the information in this report should be sufficiently qualified to be useful. As is clear from the survey results, the OReilly audience tends to use more newer, open source tools, and underrepresents non-tech industries such as insurance and energy. OReilly contentin books, online, and at conferencesis focused on technology, in particular new technology, so it makes sense that our audience would tend to be early adopters of some of the newer tools.

A final word on the self-selecting nature of the sample: differences between results in this survey and other surveys may simply arise from the samples idiosyncrasies and not from any meaningful difference. Findings from other salary survey reportsthere have been a few recently in the data spacesometimes conflict directly with our findings, but this doesnt necessarily imply that one set of findings are erroneous. Likewise, discrepancies between our own salary surveys dont necessarily imply a trend. The methodology between this years survey and last years is close enough to allow us to make some conclusions based on year-to-year differences, but only when the numbers are very strong.