Seeing that the data analytics industry is young, its not surprising to see professionals more active in moving employers often as anyone with experience becomes far more valuable to companies and can reach manager status quickly. Across all industries in Europe, 68% of people do not currently work in start-ups. Despite this, the data science market remains open-minded, with 83% saying they would consider joining one in the future. The respondents that currently work in start-up industries are primarily Technology/IT and Consulting (37%). Respondents from these two industries are also respectively the majority that would consider working for a start-up in the future (38%).
In December, we shared some insight into what a data scientist is worth, with data mainly focused on the United States, with some additional geographic data sprinkled in for good measure. With the recent release of Big Cloud's annual survey reports on data science professionals by geographic region, we now have some quality data and visualizations to help us gain a better understanding of what data scientists are worth worldwide. We will have a look at Europe this time around, specifically the skills that data science professionals report to be using, salaries for these professionals, and insights of interest from the report. Since 2016, Big Cloud has committed to producing some of the biggest and best Data Science salary reports year on year. Over 1300 responses and 33 questions later, we are proud to announce this is the most data-rich survey for Europe we have curated to date!
O'Reilly has released the results of the 2016 Data Science Salary Survey. This survey is based on data from over 900 respondents to a 64-question survey about data-related tasks, tools, and the salary they receive from doing/using them. The median salary reported in the survey was US 87,000; amongst data scientists in the US, the median salary was US 106,000. Appropriately for a survey about data science, O'Reilly doesn't merely report aggregate statistics from the survey; they fit a linear regression model for a data, and extact coefficients from the model indicative of salary "bumps" (or downgrades) attributable to demographic factors. Factors that tended to increase salary included: working in cloud computing environments; working with Python; and being older.