2015 Data Science Salary Survey

Notes

^[1]Throughout the report we use base salary; in the past we have also reported total salary, but find total salary is error-prone in a self-reporting online survey. Salary information was entered to the nearest $5,000, but quantile values cited in this report include a modifier that estimates the error lost by using rounding.

^[2]“Effect” is in quotations because without a controlled experiment we can’t assume causality: particular variables, within a margin of error, might be certain to correlate with salary, but this doesn’t mean they caused the salary to change, quite relevantly to this study, it doesn’t necessarily mean that if a variable’s value is changed someone’s salary would change (if only it were so simple!). However, depending on the variable, the degree of causality can be inferred to a greater or lesser extent. For example, with location there is a very clear and expectable variation in salary that largely reflects local economies and costs of living. If we include the variable “uses Mac OS,” we see a very high coefficient—people who use macs earn more—but it seems highly unlikely that this caused any change in salary.—More likely, the companies that can afford to pay more can also afford to buy more-expensive machines for their employees.

^[3]We should note that there are multiple variables corresponding to “student”. The group that are excluded from (all) of our salary models are the 3% that identify primarily as a student, that is, this is their job title. ...