The median data analyst makes about $98,000 –including bonuses — in the U.S., according to a new salary survey from O’Reilly Media. But data people being what they are, the report includes a regression that allows anyone to compare their salary based on 27 variables from location to experience, from tools used to gender.
The survey of 816 people (about two-thirds from the U.S.) isn’t random, and the fact that it deals with data wranglers certainly caught our eye. But a survey that actually breaks down the differences among salaries really stands out.
Why aren’t more salary surveys done this way?
Salaries have been a tricky thing in the past few years, especially for journalists. Publishing salaries of state or university workers is common at news organizations. They get lots of viewers — and a lot of push back for privacy invasion.
But others have argued that knowing everyone’s salary is the only way to insure pay equity, and that salary is based on merit not one’s ability to negotiate. It can also avoid scandals such as an $800,000 city manager in a low-income suburb of Los Angeles.
Yet even if the human resources department decided everyone’s pay should be transparent, that still doesn’t provide context — is there a good reason someone earns more?
Which is why the O’Reilly survey is important. Even with the 27 variables that contribute to salary, the regression only explains about 58 percent of the variance. Still, even the attempt to explain variance reveals some interesting findings:
- Geography matters. Not surprisingly, data scientists in California and the Northeast make more (between $17K and $26K). But working in Texas had the second-highest boost.
- Startups don’t pay well; neither does government. Analysts in education lowered the expected salary by $30K; start ups drop the salary about $17K.
- Experience counts. Every year of age and each year working with data, together adds about $2,500 to the expected salary. Using tools such as Python, Natural Language Processing, NumPy and R can *each* add $1,900 in expected salary. SQL, Python, Excel and R are the most common tools used.
- Being female hurts. The survey shows a $13K gender pay gap among data scientists — and says no differences in tools, experience or other factors account for it. See also Wage Debate at the Oscars.
Data science –whether it’s in journalism, government contracting or elsewhere — is a rapidly expanding field, which makes predicting salaries difficult. The O’Reilly survey may not be perfect, but it gives people real tools to create transparency, without invading privacy.