Roger Magoulas - 2017 European Data Science Salary Survey

by John King and Roger Magoulas

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In 2016, OReilly Media conducted a data science salary survery online. The survey contained 40questions about the respondents roles, tools, compensation,and demographic backgrounds. About 1,000 data scientists,analysts, engineers, and other professionalsworking in Data participated in thesurvey359 of them from Europeancountries. Here, wetake a deep dive into the results fromrespondents based in Europe, exploringcareer details and factors thatinfluence salary. Some key findingsinclude:

• Most of the variation in salaries can be attributed to differences in the local economy

• Data professionals who use Hadoop and Spark earn more

• Among those who use R or Python, users of both have the highest salaries

• A few technical tasks correlate with higher salaries: developing prototype models, setting up/maintaining data platforms, and developing products that depend on real-time analytics

• Respondents who use Hadoop, Spark, or Python were twice as likely to have a major increase in salary over the last three years, compared with those whose stack consists of Excel and relational databases

Note

Respondents who use Hadoop, Spark, or Python were twice as likely to have a major increase in salary over the last three years.

We hope that these findings will beuseful as you develop your career in data science.

Introduction

SINCE 2013, WE HAVE CONDUCTED AN ONLINE SALARYSURVEY FOR DATA PROFESSIONALS and published areport on our findings. US respondents typically dominatethe sample, at about 60%70%. Although many of thefindings do appear to apply to people across the globe, wethought it would be useful to show results specific to Europe,looking at finer geographical details and identifying any patternsthat seem to only apply to Europe. In this report, we pool all359 European respondents from the Data Salary Survey over a13-month period: September 2015 to October 2016.

The median salary of European respondents was 48K,but the spread was huge. For example, the top third earnedalmost four times on average as the bottom third. Such alarge variance is not surprising due to the differences in theper capita income of countries represented.

A note on currency: we requested responses about salariesand other monetary amounts in US dollars. In this report, wehave converted all amounts into euros, though many Europeanrespondents are paid in other currencies, such as pounds orrubles. Over the period in which responses were collected,there were some important shifts in exchange rates, mostnotably the fall of the pound after Brexit. However, thegeographical distribution of responses did not correlate in anymeaningful way with any period of collection (e.g., when thepound was high or low), so these currency fluctuations likelytranslate into noise rather than bias.

Note

In the horizontal bar charts throughout this report, we includethe interquartile range (IQR) to show the middle 50% ofrespondents answers to questions such as salary. One quarterof the respondents have a salary below the displayed range,and one quarter have a salary above the displayed range.

The IQRs are represented by colored, horizontal bars. On eachof these colored bars, the white vertical band represents themedian value.

Countries

THE UK WAS THE MOST WELL-REPRESENTED EUROPEANCOUNTRY, with about a quarter of the sample, followedby Germany, Spain, and the Netherlands. By far, the highestsalaries were in Switzerland, witha median salary of 117K, followedby Norway with 96K, althoughthe latter figure is only based onfive respondents. Among countriesrepresented by more than just ahandful of respondents, the UK hadthe second-highest median salary:63k (53).

Even within Western Europe, there was significant variationin salary. While UK, Swiss, and Scandinavian salaries weresignificantly higher than the Western European median of 54K, Spanish and Italian respondents tended to have muchlower salaries (35K). Portugal was somewhat of an outlier inWestern Europe, with a median of 22K. The median salariesof Germany, the Netherlands, andFrance were close to the regionalmedian (about 53K).

Salaries drop dramatically as wemove south and east. The mediansalary of respondents from Centraland Eastern Europe was 17K. Russiaand Poland, the two most well-representedcountries in this half of thecontinent, also had median salaries of 17K: unlike in the west,Eastern European salaries appeared to be fairly consistent,even across national borders.

Note

Unlike in the west, EasternEuropean salaries appearedto be fairly consistent, evenacross national borders.

Salary Versus GDP

NATIONAL MEDIAN SALARIES SHOULD BE EXPECTEDTO VARY according to the economicconditions of the country, so thequestion becomes: given a countryseconomy (in particular, its per capitaGDP), do the salaries of data scientistsand engineers vary? Here, we plot percapita GDP and median salary of eachcountry in the sample. The resultinggraph is remarkably linear, with outlierslargely explained by small sample size:Greece, for example, has a higher-than-expected median salary given arelatively low per capita GDP, but this isbased on just one respondent.

One shortcoming of this plot is that it does not take into accountyears of experience, which turnsout to be very uneven in the sampleamong different countries. In particular,respondents from Western Europetended to be much more experienced(with an average of seven years) thanrespondents from Eastern Europe(with an average of four years).Since experience correlates with salary,the West-East salary difference isexaggerated due to this experiencedifferential.