Introduction
FOR THE FOURTH YEAR RUNNING, we at O’Reilly Media have collected survey data from data scientists, engineers, and others in the data space, about their skills, tools, and salary. Across our four years of data, many key trends are more or less constant: median salaries, top tools, and correlations among tool usage. For this year’s analysis, we collected responses from September 2015 to June 2016, from 983 data professionals.
In this report, we provide some different approaches to the analysis, in particular conducting clustering on the respondents (not just tools). We have also adjusted the linear model for improved accuracy, using a square root transform and publicly available data on geographical variation in economies. The survey itself also included new questions, most notably about specific data-related tasks and any change in salary.
Salary: The Big Picture
The median base salary of the entire sample was $87K. This figure is slightly lower than in previous years (last year it was $91K), but this discrepancy is fully attributable to shifts in demographics: this year’s sample had a higher share of non-US respondents and respondents aged 30 or younger. Three-fifths of the sample came from the US, and these respondents had a median salary of $106K.
Understanding Interquartile Range
For a number of survey questions, we show graphs of answer shares and the median salaries of respondents who gave particular answers. While median salary is probably the best number to compare how much two groups of people make, it doesn’t say anything about the spread or variation of salaries. In addition to median, we also show the interquartile range (IQR)—two numbers that delineate salaries of the middle 50%. This range is not a confidence interval, nor is it based on standard deviations.
As an example, the IQR for US respondents was $80K to $138K, meaning one quarter of US respondents had salaries lower than $80K and one quarter had salaries higher than $138K. Perhaps more illustrative of the value of the IQR is comparing the US Northeast and Midwest: the Northeast has a higher median salary ($105K vs. $98K) but the third quartile cutoffs are $133K for the Northeast and $138K for the Midwest. This indicates that there is generally more variation in Midwest salaries, and that among top earners—salaries might be even higher in the Midwest than in the Northeast.
How Salaries Change
We also collected data on salary change over the last three years. About half of the sample reported a 20% change, and the salary of 12% of the sample doubled. We attempted to model salary change with other variables from the survey, but the model performed much more poorly, with an R2 of just 0.221. Many of the same significant features in the salary regression model also appeared as factors in predicted salary change: Spark/Unix, high meeting hours, high coding hours, and building prototype models, all predict higher salary growth, while using Excel, gender disparity, and working at an older company predict lower salary growth. Geography also correlated positively with salary change, meaning that
Assessing Your Salary
To use the model for you own salary, refer to the full model in Appendix B, and add up the coefficients that apply to you. Once all of the constants are added, square the result for a final salary estimate (note: the coefficients are not in dollars). The contribution of a particular coefficient to the eventual salary estimate depends on the other coefficients: the higher the salary, the higher the contribution of each coefficient.
For example, the salary difference between a junior data scientist and a senior architect will be greater in a country with high salaries than somewhere with lower salaries.
