2015 Data Science Salary Survey

Wrapping Up

UNDERSTANDING SALARY is a tricky business: the rules that determine it can change from year to year (for example, not knowing Spark was okay in 2010), we’re not supposed to know what our colleagues make (for good reason), and it’s extremely important (we all have to eat). Statistics from on an anonymous online survey based on a self-selected sample doesn’t exactly put the “science” into “data science,” but such research can still be valuable—and let’s face it, much of the other information that might inform one’s understanding of industry trends is in the same assumption-violating category.

Only about 40% of the variation in the survey sample’s salaries is explained by our models, but this is nevertheless a decent starting point for practitioners to estimate their worth and for employers to understand what is reasonable compensation for their employees. It would be unwise to assume correlation is causation: learning a given tool with a hefty coefficient may not instantly trigger a raise, and whatever you take from this report, it should not be a desire to needlessly stretch tomorrow’s meeting to put you in the four hours/day bracket. Still, it seems likely that in the long run knowing the highest paying tools will increase your chances of joining the ranks of the highest paid.

In future editions of the Salary Survey, we may look to better understand roles and the shift to merge open source and non-open source tools (such as R).

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