The type of work respondents do was captured through four different types of questions:
For every task, respondents chose from three options: no engagement, minor engagement, or major engagement.
The task with the greatest impact on salary (i.e., the greatest coefficient) was developing prototype models. Respondents who indicated major engagement with this task received on average a $7.4K boost, based on our model. Even minor engagement in developing prototype models had a +4.4 coefficient.
When both tasks and job titles are included in the training set, job title “wins” as a better predictor of salary. It’s notable however, that titles themselves are not necessarily accurate at describing what people do. For example, even among architects there was only a 70% rate of major engagement in planning large software projects—a task that theoretically defines the role. Since job title does perform well as a salary predictor, despite this inconsistency, it may be that “architect,” for example, is a symbol of seniority as much as anything else.
Respondents with “upper management” titles—mostly C-level executives at smaller companies, directors and VPs—had a huge coefficient of +20.2. Engagement in tasks associated with managerial roles also had a positive impact on salary, namely: organizing team projects (+9.7), identifying business problems to be solved with analytics (+1.5/+6.7), and communicating with people outside the company (+5.4).
People who spend more time in meetings tend to make more. This is the variable we often use as a reminder that the model does not guarantee that the relationships between significant variables and salary are causative: if someone starts scheduling many meetings (and doesn’t change anything else in their workday) it is unlikely that this will lead to anything positive, much less a raise.1
The highest median salaries belong to those who code 4–8 hours per week; the lowest to those who don’t code at all. Notably, only 8% of the sample reported that they don’t code at all, significantly down from last year’s 20%. Coding is clearly an integral part of being a data scientist.
1 Of course, we haven’t actually tested this. If you try it out, let us know how it goes.