2016 Data Science Salary Survey

Executive Summary

IN THIS FOURTH EDITION of the O’Reilly Data Science Salary Survey, we’ve analyzed input from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states. Through the results of our 64-question survey, we’ve explored which tools data scientists, analysts, and engineers use, which tasks they engage in, and of course—how much they make.

Key findings include:

• Python and Spark are among the tools that contribute most to salary.
• Among those who code, the highest earners are the ones who code the most.
• SQL, Excel, R and Python are the most commonly used tools.
• Those who attend more meetings, earn more.
• Women make less than men, for doing the same thing.
• Country and US state GDP serves as a decent proxy for geographic salary variation (not as a direct estimate, but as an additional input for a model).
• The most salient division between tool and tasks usage is between those who mostly use Excel, SQL, and a small number of closed source tools—and those who use more open source tools and spend more time coding.
• R is used across this division: even people who don’t code much or use many open source tools, use R.
• A secondary division emerges among the coding half— separating a younger, Python-heavy data scientist/analyst group, from a more experienced data scientist/engineer cohort that tends to use a high number of tools and earns the highest salaries.

To see our complete model and input your own metrics to predict ...