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2016 Data Science Salary Survey by Roger Magoulas, John King

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Wrapping Up: What to Consider Next

THE REGRESSION MODEL WE USE to predict salary describes relationships between variables, but not where the relationships come from, or whether they are directly causative. For example, someone might work for a company with a colossal budget that can afford high salaries and expensive tools, but this doesn’t mean that their high salary is driven up by their tool choice.

Of course, it’s not so simple with salary. When tools become industry standards, employers begin to expect them, and it can hurt your chances of landing a good job if you are missing key tools: it’s in your interest to keep up with new technology. If you apply for a job at a company that is clearly interested in hiring someone who knows a certain tool, and this tool is used by people who earn high salaries, then you have leverage knowing that it will be hard for them to find an alternative hire without paying a premium.

This information isn’t just for the employees, either. Business leaders choosing technologies need to consider not just the software costs, but labor expenses as well. We hope that the information in this report will aid the task of building estimates for such decisions.

If you made use of this report, please consider taking the 2017 survey. Every year we work to build on the last year’s report, and much of the improvement comes from increased sample sizes. This is a joint research effort, and the more interaction we have with you, the deeper we will be able to ...

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