2015 Data Science Salary Survey

How You Spend Your Time

ANOTHER SET OF QUESTIONS on the survey asked for the approximate amount of hours spent on certain tasks, such as data cleansing, ETL, and machine learning. For managers, directors, VPs, and executives (even at small companies), the task breakdown is very different, as we would expect: fewer technical tasks, more meetings. Removing their responses gives us a general idea of how people spend their time in the data space.

Even among non-managers, it appears that the more time spent in meetings, the more a data scientist (/analyst/engineer) earns. About half of the respondents report spending at least one hour per day on average in a meeting, with 12% spending at least four hours per day in meetings. This pattern is confirmed when we add the task features to the salary model.

Among technical tasks, basic exploratory analysis occupies more time than any other, with 46% of the sample spending one to three hours per day on this task and 12% spending four hours or more. After this, data cleaning eats up the most hours: 39% spend at least one hour per day cleaning data.

To put these hour figures into context, it may help to know the length of the entire work week. Most (75%) of respondents work between 40 and 50 hours per week, with the remaining 25% split evenly between those who work fewer than 40 and more that 50 hours per week. Working longer hours does, in fact, correspond to higher salary.

A final variable will be introduced for the second salary model: bargaining ...