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2016 Data Science Salary Survey
book

2016 Data Science Salary Survey

by John King, Roger Magoulas
September 2016
Beginner
40 pages
46m
English
O'Reilly Media, Inc.
Content preview from 2016 Data Science Salary Survey

How You Spend Your Time

Importance of Tasks

The type of work respondents do was captured through four different types of questions:

  • involvement in specific tasks
  • job title
  • time spent in meetings
  • time spent coding

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.

Relevance of Job Titles

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 ...

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Publisher Resources

ISBN: 9781492049029Errata Page