2017 european data science salary survey pdf pdf
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17 European Data Science Salary Survey
Tools, Trends, What Pays (and What Doesn’t) for Data Professionals in Europe
Take the Data Science Salary Survey
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hope you’ll participate today.2017 European Data Science Salary Survey
Tools, Trends, What Pays (and What Doesn’t)
for Data Professionals in EuropeJohn King and Roger Magoulas REVISION HISTORY FOR THE FIRST EDITION 2017 EUROPEAN DATA SCIENCE SALARY SURVEY 2017-02-10: First Release by John King and Roger Magoulas
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2017-02-10. First Edition
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2017 EUROPEAN DATA SCIENCE SALARY SURVEY Table of Contents
2017 European Data Science Salary Survey ........................................ i
2017 EUROPEAN DATA SCIENCE SALARY SURVEY HERE WE TAKE A DEEP DIVE YOU CAN PRESS ACTUAL BUTTONS (and earn our sincere
INTO THE RESULTS FROM
gratitude) by taking the 2017 survey—it only takes about 5 to 10 minutes, RESPONDENTS BASED IN EUROPE, EXPLORING CAREER and is essential for us to continue to provide this kind of research. DETAILS AND FACTORS THAT
INFLUENCE SALARY
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Executive Summary
■
IN 2016, O’REILLY MEDIA CONDUCTED A DATA SCIENCE Among those who use R or Python, users of both SALARY SURVEY ONLINE. The survey contained 40 have the highest salaries
questions about the respondents’ roles, tools, compensation,
■ A few technical tasks correlate with higher
and demographic backgrounds. About 1,000 data scientists,
salaries: developing prototype
analysts, engineers, and other profession-
models, setting up/maintaining
als working in Data participated in the
data platforms, and developing
Respondents who use
survey—359 of them from European
products that depend on real-time
countries. Here, we
analytics
Hadoop, Spark, or
take a deep dive into the results from
■ Respondents who use Hadoop,
respondents based in Europe, explor- Python were twice as
Spark, or Python were twice as
ing career details and factors that
likely to have a major increase in
likely to have a major
influence salary. Some key findings
salary over the last three years,
include:
increase in salary over
compared with those whose stack consists of Excel and■ Most of the variation in salaries the last three years. relational databases can be attributed to differences in the local economy
We hope that these findings will be
■ Data professionals who use Hadoop and useful as you develop your career in data science.
Spark earn more
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Introduction
SINCE 2013, WE HAVE CONDUCTED AN ONLINE SALARY respondents are paid in other currencies, such as pounds or
SURVEY FOR DATA PROFESSIONALS and published a rubles. Over the period in which responses were collected,report on our findings. US respondents typically dominate there were some important shifts in exchange rates, most the sample, at about 60%–70%. Although many of the notably the fall of the pound after Brexit. However, the findings do appear to apply to people across the globe, we geographical distribution of responses did not correlate in any thought it would be useful to show results specific to Europe, meaningful way with any period of collection (e.g., when the looking at finer geographical details and identifying any patterns pound was high or low), so these currency fluctuations likely that seem to only apply to Europe. In this report, we pool all translate into noise rather than bias. 359 European respondents from the Data Salary Survey over a 13-month period: September 2015 to October 2016.
The median salary of European respondents was €48K,
In the horizontal bar charts throughout this report, we include
but the spread was huge. For example, the top third earned
the interquartile range (IQR) to show the middle 50% of
almost four times on average as the bottom third. Such a
respondents’ answers to questions such as salary. One quarter
large variance is not surprising due to the differences in the
of the respondents have a salary below the displayed range, per capita income of countries represented. and one quarter have a salary above the displayed range.
A note on currency: we requested responses about salaries
The IQRs are represented by colored, horizontal bars. On each
and other monetary amounts in US dollars. In this report, we
of these colored bars, the white vertical band represents the have converted all amounts into euros, though many European median value.
BASE SALARY (EURO) SHARE OF RESPONDENTS
€0K €20K €40K €60K
€80K (EUROS) y
€100K alar
€120K ase S B
€140K €160K €180K > €180K
0% 5% 10% 15% 20% 25% 30% 35% 40% Share of Respondents
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Countries
€
THE UK WAS THE MOST WELL-REPRESENTED EUROPE-
54K, Spanish and Italian respondents tended to have much
AN COUNTRY, with about a quarter of the sample, followed
lower salaries (€35K). Portugal was somewhat of an outlier in by Germany, Spain, and the Netherlands. By far, the highest Western Europe, with a median of €22K. The median salaries salaries were in Switzerland, with of Germany, the Netherlands, and a median salary of €117K, followed France were close to the regional
Unlike in the west, Eastern
by Norway with €96K, although median (about €53K). the latter figure is only based on
European salaries appeared Salaries drop dramatically as we
five respondents. Among countries move south and east. The median
to be fairly consistent, even
represented by more than just a salary of respondents from Central handful of respondents, the UK had and Eastern Europe was €17K. Russia
across national borders.
the second-highest median salary: and Poland, the two most well-rep-€ 63k (£53). resented countries in this half of the
Even within Western Europe, there was significant variation continent, also had median salaries of €17K: unlike in the west, in salary. While UK, Swiss, and Scandinavian salaries were
Eastern European salaries appeared to be fairly consistent, significantly higher than the Western European median of even across national borders.
COUNTRIES COUNTRIES SHARE OF RESPONDENTS SHARE OF RESPONDENTS SHARE OF RESPONDENTS
United Kingdom United Kingdom United Kingdom United Kingdom Germany Germany Germany Germany Spain Spain Spain Spain
Netherlands Netherlands Netherlands Netherlands France France France France y tr oun
Ireland Ireland Ireland Ireland C
Russia Russia Russia Russia Switzerland Switzerland Switzerland Switzerland Poland Poland Poland Poland
Italy Italy Italy Italy 0% 0% 0% 5% 5% 5% 10% 10% 10% 15% 15% 15% 20% 20% 20% 25% 25% 25% 30% 30% 30% 0% 5% 10% 15% 20% 25% 30%
Share of Respondents
COUNTRIES SALARY MEDIAN AND IQR* (EURO) United Kingdom Germany
Spain Netherlands y
France tr oun C
Ireland Russia Switzerland Poland
Italy €0K €30K €60K €90K €120K € 150K
Range/Median (Euro)
*The interquartile range (IQR) is the middle 50% of respondents' salaries. One quarter of respondents have a salary below this range, one quarter have a salary above this range.
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Salary Versus GDP NATIONAL MEDIAN SALARIES SHOULD BE EXPECTED One shortcoming of this plot is that it does not take into ac- TO VARY according to the economic
count years of experience, which turns conditions of the country, so the out to be very uneven in the sample
The question becomes,
question becomes: given a country’s among different countries. In particu- economy (in particular, its per capita lar, respondents from Western Europe
given a country’s
GDP), do the salaries of data scientists tended to be much more experienced
economy (in particular,
and engineers vary? Here, we plot per (with an average of seven years) than capita GDP and median salary of each respondents from Eastern Europe
its per capita GDP),
country in the sample. The resulting (with an average of four years). graph is remarkably linear, with outliers do the salaries of Since experience correlates with salary, largely explained by small sample size: the West-East salary difference is
data scientists and
Greece, for example, has a high- exaggerated due to this experience er-than-expected median salary given a differential.
engineers vary?
relatively low per capita GDP, but this is based on just one respondent.
SALARY VERSUS GDP The size of each circle represents the number of respondents from the country in the sample. MEDIAN SALARY VERSUS PER CAPITA GDP Source for per capita GDP: https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita
€80K Switzerland Norway s) o
€60K ur f E
Denmark Ireland Sweden thousands o
Austria €40K Netherlands
United Kingdom Belgium Finland a GDP (
Germany France apit
Italy Per c
Spain €20K
Slovenia Portugal Greece Estonia
Czech Republic Hungary Slovakia Poland Croatia
Turkey Romania Russia Belarus
Serbia Armenia €0K €0K €20K €40K €60K €80K €100K €120K €140K
Median salary of data scientists / engineers (thousands of Euros)
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Company Size
COMPARED TO THE WORLDWIDE SAMPLE, THE SUBSAMPLE FROM EUROPE TENDED TO COME FROM
SMALLER COMPANIES. While 45% of US respondents were
from companies with over 2,500 employees, only 35% of European respondents were from such companies. This number rises to 39% if we consider only those from Western Europe; only 13% of respondents from Central/Eastern Europe were from large companies.
Largely because of the East-West split, salaries at larger com- panies tend to be high: the 19% of respondents from compa- nies with over 10,000 employees had a median salary of €61K. In contrast, the half of the sample that was from companies with 2 to 500 employees had a median salary of €43K.
COMPANY SIZE SHARE OF RESPONDENTS
19% 10,000+
16% 8% 2,501 – 10,000 1,001 – 2,500
5% 501 – 1,000
SALARY MEDIAN AND IQR*
22%
1
101 – 500- – 2
25
s- – 26 100
ee y
- – 101 500
17% f Emplo
- – 501 1,000 26 – 100
- – 1,001 2,500
Number o
- – 2,501 10,000 10,000 +
11% €0K €20K €40K €60K €80K €100K €120K
2 – 25 Range/Median
1%
1
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Industry
A PLURALITY OF RESPONDENTS (20%) WORKED IN CONSULTING, after which the top industries were software (18%), banking/finance (10%), and retail/ecommerce (9%).
These figures are very similar to those of the worldwide sample. As with company size, the differences in salaries among in- dustries was largely attributable to geography. Manufacturing, insurance, and publishing/media were all overrepresented by countries with higher salaries. One exception to this was bank- ing/finance, which had a high median salary of €58K and did not correlate with a particular country or region: data profes- sionals in banking do appear to earn more.
INDUSTRY SHARE OF RESPONDENTS
2%
INSURANCE 3%
ENTERTAINMENT 3%
OTHER 5%
PUBLISHING / MEDIA 5% MANUFACTURING / HEAVY INDUSTRY
5% CARRIERS / TELECOMMUNICATIONS
6% EDUCATION
6% HEALTHCARE / MEDICAL
6% ADVERTISING / MARKETING / PR
9% RETAIL / ECOMMERCE
10% BANKING / FINANCE
18% SOFTWARE
21% CONSULTING
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Tools
THE TOP FOUR TOOLS FROM EUROPEAN RESPONDENTS those who used more than 10 tools had a median salary
WERE EXCEL, SQL, R, AND PYTHON , each used by over of €53K.half of all respondents. These four tools have kept their top Since there is significant overlap between users of individu- positions in every Data Salary Survey we have conducted, and al tools, it is useful to consider mutually exclusive groups of there does not appear to be any sign of this changing. Almost respondents based on tool usage. The groups we will define every respondent reported using at least one, and about half here are based on a simple set of rules, but using a clustering the sample used three or all four. algorithm would produce very
Commonly used tools with similar results. The rules are:
Commonly used tools with
above-average salaries include 1) If someone used Spark or
Scikit-learn (whose users have
above-average salaries include
Hadoop, we call them “Hadoop” a median salary of €52K), Spark
Scikit-learn (whose users have
2) If someone (not in the Hadoop (€55K), Hive (€57K), and Scala group) uses R and/or Python,
a median salary of (€52K),
(€70K). Readers may notice that they are labeled “R+Python,” most tools have a higher median
Spark (€55K), Hive (€57K), and
“R-only,” or “Python-only,,” as salary than appropriate
Scala (€70K).
the sample-wide median salary3) Everyone who uses SQL and/ of €48K. This is because respon- or Excel (usually both), we call dents who use lots of tools tend to “SQL/Excel” earn more (and they are counted in a large number of tool salary medians). The 43% of respondents who used no
The five resulting groups each contain between 13% more than 10 tools had a median salary of €43K, while and 26% of the sample. The Hadoop group reported the
TOOLS SHARE OF RESPONDENTS
Excel SQL R Python ggplot MySQL
Scikit-learn Bash Matplotlib Spark
Microsoft SQL Server PostgreSQL Oracle Tableau
Hive D3 Java JavaScript
Shiny Spark MlLib ool
Apache Hadoop T
Cloudera ElasticSearch Scala MongoDB
Visual Basic/VBA QlikView Matlab Hortonworks
SQLite Google Charts Impala Kafka
Hbase C C++ Power BI
Weka 0% 10% 20% 30% 40% 50% 60% 70% Share of Respondents
TOOLS SALARY MEDIAN AND IQR*
T ool Range/Median
€0K €20K €40K €60K €80K €100K Weka Power BI C++
C Hbase Kafka Impala
Google Charts SQLite Hortonworks Matlab
QlikView Visual Basic/VBA MongoDB Scala
ElasticSearch Cloudera Apache Hadoop Spark MlLib
Shiny JavaScript Java D3 Hive Tableau
Oracle PostgreSQL Microsoft SQL Server Spark
Matplotlib Bash Scikit-learn MySQL ggplot Python
R SQL Excel
2017 EUROPEAN DATA SCIENCE SALARY SURVEY
highest salaries (median: €56K), while the R-only group had the lowest (€42K). However, this doesn’t mean that knowing R means less pay: respondents using Python and R earned slightly more than those using Python and not R.
Aside from salary, one important difference between the groups is experience. The SQL/Excel group—in other words, those who don’t use Python, R, Spark, or Hadoop—was more experienced than the other groups (8.3 years on average), followed by the R-only (7.3 years), Hadoop (6.3 years), Python-only (6 years), and Python+R groups (5.2 years). Since we expect more-experienced data professionals to earn higher salaries, the median salary of €46K for the SQL/Excel group is actually quite low, while the €48K of the Python-R group is high.
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Tasks WE ALSO ASKED FOR INFORMATION ABOUT WORK Tasks that correlate most strongly with high salaries are
TASKS: this is meant to dig a little deeper than what we those that involve management and business decisions, such
can glean from a job title. Respondents could say they had as “communicating findings to business decision-makers,” “major” or “minor” involvement in each task. For the most “identifying business problems to be solved with analytics,” part, tasks that correlate positively with salary also correlate “organizing and guiding team projects,” and “communicat- positively with years of experi- ing with people outside of your ence (and often are clearly asso- company”. The median salaries ciated with being a manager). of respondents who reported
Tasks that correlate most
major involvement in these tasks Among the most common
strongly with high salaries are
were €54K, €56K, €66K, and tasks were “basic exploratory € 55K, respectively.
those that involve management
data analysis,” “data cleaning,” “creating visualizations,” and
Aside from management and
and business decisions.
“conducting data analysis to business strategy, several answer research questions,” each technical tasks stood out for with 85%–93% of the sample above-average salaries: as a major or minor task. Data cleaning has the unfavorable “developing prototype models” (major involvement: €52K), distinction of being the only task for which each level of “setting up/maintaining data platforms” (€50K), and involvement means less pay: those with major involvement “developing products that depend on real-time analytics” earn less than those with minor involvement, who in turn (€62K). For each of these tasks, respondents who reported earn less than those who never clean data. However, this may major involvement earned more than those who reported have more to do with the fact that more-experienced data minor involvement, and those who reported minor professionals (who we know earn more) tend to do less data involvement earned more than those who did not cleaning. engage in these tasks at all.
RESPONDENT CATEGORIES WHICH OF THE FOLLOWING MOST ACCURATELY
BASED ON TOOL USAGE DESCRIBES THE NEXT STEP YOU WOULD LIKE TO
TAKE TO ADVANCE YOUR CAREER? SHARE OF RESPONDENTS SHARE OF RESPONDENTS26% 41%
HADOOP / SPARK LEARN NEW TECHNOLOGY/SKILLS
22% PYTHON+R
20% WORK ON MORE INTERESTING/
18%
IMPORTANT PROJECTS R ONLY 13%
18% PYTHON ONLY MOVE INTO LEADERSHIP ROLES
12% 19% SWITCH COMPANIES SQL/EXCEL
(NO PY/R) 6% START YOUR OWN COMPANY
(EUROS) SALARY MEDIAN AND IQR (EUROS) SALARY MEDIAN AND IQR
Hadoop / Spark Learn new technology/skills Python+R
Work on more interesting/ important projects R only tep t S
Move into leadership roles Python only x ool Usage
T Ne
SQL/Excel (no Py/R) Switch companies
100
€20K €40K €60K €80K Start your own company Range/Median
€0K €20K €40K €60K €80K €100K
T ask Number of Respondents
0% 10% 20% 30% 40% 50% 60% 70% Using dashboards and spreadsheets (made by others) to make decisions Developing products that depend on real-time data analytics Setting up/maintaining data platforms
Developing data analytics software Teaching/training others Planning large software projects or data systems Communicating with people outside your company
Developing dashboards Organizing and guiding team projects ETL Collaborating on code projects (reading/editing others' code, using git)
Implementing models/algorithms into production Identifying business problems to be solved with analytics Developing prototype models Feature extraction
Creating visualizations
Data cleaning
Communicating findings to business decision-makers Conducting data analysis to answer research questionsBasic exploratory data analysis RESPONDENTS COUNTED IF THEY SAID THEY HAVE "MAJOR INVOLVEMENT" IN THIS TASK TASKS
TASKS SALARY MEDIAN AND IQR*
Basic exploratory data analysis Conducting data analysis to answer research questions Communicating findings to business decision-makers
Data cleaning
Creating visualizations Feature extraction Developing prototype models Identifying business problems to be solved with analytics
Implementing models/algorithms into production Collaborating on code projects (reading/editing others' code, using git) ETL ask T
Organizing and guiding team projects Developing dashboards Communicating with people outside your company Planning large software projects or data systems
Teaching/training others Developing data analytics software Setting up/maintaining data platforms Developing products that depend on real-time data analytics
Using dashboards and spreadsheets (made by others) to make decisions €0K €20K €40K €60K €80K €100K Range/Median (Euro)
2017 EUROPEAN DATA SCIENCE SALARY SURVEY
Coding and MeetingsFOR TWO BROADER TASKS, coding and attending meetings,
we asked respondents for more detail: namely, how much time they spend on them. As we have consistently seen, attending meetings correlates with salary: respondents who spend over 20 hours per week in meetings earn more than those who spend 9–20 hours, who in turn earn more than those whose spend 4–8 hours per week in meetings, and so on. This is unlikely to be a direct causal relationship, but rather both are effects of a shared cause (such as working in management). As for coding, the highest earners were those who don’t code at all, but that’s because they tended to be managers. There is a dip in salaries among respondents who code over 20 hours per week, but this is explained by the fact that this group was, on average, less experienced than the rest of the sample. Within the middle groups—those who code 1–20 hours per week—there was not much variation in pay.
TIME SPENT CODING SHARE OF RESPONDENTS
TIME SPENT IN MEETINGS SHARE OF RESPONDENTS
1 TO 3 HOURS / WEEK 2% NONE
1 TO 3 HOURS / WEEK 9% NONE
4 TO 8 HOURS / WEEK 10%
9 TO 20 HOURS / WEEK 23%
36%
23% OVER 20 HOURS / WEEK
SALARY MEDIAN AND IQR (EUROS)
SALARY MEDIAN AND IQR (EUROS)
Hour s C oding
Range/Median
9 TO 20 HOURS / WEEK 43%
23%
3% OVER 20 HOURS / WEEK
Hour s in Meetings Range/Median
4 to 8 hours / week 1 to 3 hours / week None
€20K €40K €60K €80K €100K €120K Over 20 hours / week 9 to 20 hours / week
4 to 8 hours / week 1 to 3 hours / week None
€0K €20K €40K €60K €80K €100K €120K Over 20 hours / week 9 to 20 hours / week
4 TO 8 HOURS / WEEK 29%
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Salary Change
AN ALTERNATIVE METRIC TO CURRENT SALARY is the A final question asked respondents about the next step they
amount that one’s salary changed in the last three years. Most would like to take in their career. The top response was “learn respondents’ salaries grew at least a little in the last three years, new technology/skills” and respondents who gave this answer and about a third of the sample saw tended to be less experienced (5.5 their wages rise by 50% or more over years on average) and have smaller this period. This latter group tended to Most respondents’ salaries salaries (€40K median) than the rest be less experienced, with an average of of the sample.
grew at least a little in the
4.4 years of experience (compared to Respondents who said they would
7.6 years among those whose salaries
last three years
like to move into leadership roles did not grow by 50% or more). had salaries far above average
For Spark/Hadoop and Python-only (€65K median). The other top users, we use the tool-defined groups from page 8. They responses were “work on more interesting/important were most likely to have had 50% or more wage growth projects,” “switch companies,” and “start your own (40% and 44% of them did, respectively). Respondents who company”. Respondents who work in the healthcare did not use Hadoop, Python, or R (the “SQL/Excel” group) industry were far more likely to choose “switch companies” were the least likely: only 19% of them reported a 50% rise (33%) than respondents from other industries (11%). in their salaries.
PERCENTAGE CHANGE IN SALARY
6%
- 20% TO +30%
OVER LAST THREE YEARS SHARE OF RESPONDENTS
7%
- 30% TO +40%
5%
- 40% TO +50%
11%
+10% TO +20%
6%
- 0% TO +10%
11%
- 50% TO +75%
17%
NO CHANGE
5%
- 75% TO +100% (DOUBLE)
7%
NEGATIVE CHANGE
10%
- 100% TO +200% (TRIPLE)
6%
OVER TRIPLE
7%
N//A (SALARY WAS ZERO)
2017 EUROPEAN DATA SCIENCE SALARY SURVEY Conclusion
software costs, but labor expenses as well. We hope that
THE PURPOSE OF OUR SALARY SURVEYS and the
the information in this report will aid the task of building reports based on them is to provide an annual, data-driven estimates for such decisions. snapshot of how much professionals in your field make, and to expose details of their
If you made use of this report, work and career. There are please consider taking the online
Business leaders choosing
plenty of resources out there survey. Every year, we work to that can give an idea of how build on the last year’s report,
technologies need to consider
much a data scientist can and much of the improvement
not just the software costs, but
expect to earn or which comes from increased sample software tools are on the rise, sizes. This is a joint research
labor expenses as well.
but there aren’t many places effort, and the more interaction where these data points are we have with you, the deeper integrated into one report. we will be able to explore the data science space in Europe.Thank you! This information isn’t just for employees, either. Business leaders choosing technologies need to consider not just the
We need your data.
To stay up to date on this research, your participation is critical. The survey is now open for the 2017 report, and if you can spare just 10 minutes of your time, we encourage you to take the survey. How do data science salaries for people in Europe compare to their counterparts in the rest of the world? Among the more than 1000 people who responded to O’Reilly’s 2016 Data Science Salary Survey, 359 live and work in various European countries as data scientists, analysts, engineers, and related professions.
This report takes a deep dive into the survey results from respondents in various regions of Europe, including the tools they use, the compensation they receive, and the roles they play in their respective organizations. Even if you didn’t take part in the survey, you can still plug your own information into the survey’s simple linear model to see where you fit. n With this report, you’ll learn: n How salaries vary by country and specific regions in Europe n The average size of the companies respondents work for, according to region n
How a respondent’s salary is affected by their country’s gross domestic product
The type of industry they work for, including software, banking and finance, and
n retail and ecommerce Which tools are most commonly used vs the tools used by respondents with n above-average salaries The major and minor tasks that respondents perform John King is a data scientist at O’Reilly Media. Having previously worked on survey-based sociolinguistic research in the Republic of Georgia, he now runs surveys at O’Reilly, using the results not just for internal use but also to share his findings with the public.Roger Magoulas is Director of Research for O’Reilly Media.
To stay up to date on this research, your participation is
ISBN: 978-1-491-97750-7
crucial. The survey is now open for the 2017 report; please take just 5 to 10 minutes to participate in the survey oreilly.com/ideas/take-the-2017-data-science-salary-survey