2016 Data Science Salary Survey

Salary: The Big Picture

The median base salary of the entire sample was $87K. This
figure is slightly lower than in previous years (last year it
was $91K), but this discrepancy is fully attributable to shifts
in demographics: this year’s sample had a higher share of non-US respondents and respondents aged 30 or younger.
Three-fifths of the sample came from the US, and these
respondents had a median salary of $106K.

Understanding Interquartile Range

For a number of survey questions, we show graphs of answer
shares and the median salaries of respondents who gave
particular answers. While median salary is probably the best
number to compare how much two groups of people make, it
doesn’t say anything about the spread or variation of salaries.
In addition to median, we also show the interquartile range
(IQR)—two numbers that delineate salaries of the middle
50%. This range is not a confidence interval, nor is it based
on standard deviations.

As an example, the IQR for US respondents was $80K to
$138K, meaning one quarter of US respondents had salaries
lower than $80K and one quarter had salaries higher than
$138K. Perhaps more illustrative of the value of the IQR is
comparing the US Northeast and Midwest: the Northeast has
a higher median salary ($105K vs. $98K) but the third quartile cutoffs are $133K for the Northeast and $138K for the Midwest.
This indicates that there is generally more variation in
Midwest salaries, and that among top earners—salaries might
be even higher in the Midwest than in the Northeast.

Assessing Your Salary

To use the model for you own salary, refer to the full model in
Appendix B: The Regression Model, and add up the coefficients that apply to you.
Once all of the constants are added, square the result for a
final salary estimate (note: the coefficients are not in dollars).
The contribution of a particular coefficient to the eventual
salary estimate depends on the other coefficients: the higher
the salary, the higher the contribution of each coefficient.

For example, the salary difference between a junior data scientist
and a senior architect will be greater in a country with
high salaries than somewhere with lower salaries.

Impact of Geography

Geography has a huge impact on salary, but is not adequately
captured due to sample size. For example, if a country is represented by only one or two respondents, this isn’t enough to justify
giving the country its own coefficient. For this reason, we use
broad regional coefficients (e.g., “Asia” or “Eastern Europe”),
keeping in mind however that economic differences within a
region are huge, and thus the accuracy of the model suffers.

To get around this problem, we’ve used publicly available
records of per capita GDP of countries and US states. While
GDP itself doesn’t translate to salary, it can serve a proxy
function for geographic salary variation. Note that we use
per capita GDP on the state and country level; therefore the
model is likely to produce an inaccurate estimate with GDP
figures for smaller geographic units.

Two exceptions were made to the GDP data before incorporating
it into the model. The per capita GDP of Washington DC
is $181K—much greater than in neighboring Virginia ($57K)
and Maryland ($60K). Many (if not most) data science jobs in
Maryland and Virginia are actually in the greater DC metropolitan
area, and the survey data suggest that average data science
salaries in these three places are not radically different from
each other. Using the true $181K figure would produce gross overestimates for DC salaries, and so the per capita GDP figure
for DC was replaced with that of Maryland, $60K.

The other exception is California. In all of the salary surveys we
have conducted, California has had the highest median salary
of any state or country, even though its per capita GDP ($62K)
is not ranked so high (nine states have higher per capita GDPs,
as do two countries that were represented in the sample,
Switzerland and Norway). The anomaly is likely due to the San
Francisco Bay Area, where, depending on how the region is
defined, per capita GDP is $80K–$90K. As a major tech center,
the Bay Area is likely overrepresented in the sample, meaning
that the geographic factor attributable to California should be
pushed upward; an appropriate compromise was $70K.

Considering Gender

There is a difference of $10K between the median salaries of
men and women. Keeping all other variables constant—same
roles, same skills—women make less than men.

Age, Experience, and Industry

Experience and age are two important variables that influence
salary. The coefficient for experience (+3.8) translates to an
increase of $2K–$2.5K on average, per year of experience. As
for age, the biggest jump is between people in their early and
late 20s, but the difference between those aged 31–65 and
those over 65 is also significant.

We also asked respondents to rate their bargaining skills on
a scale of 1 to 5, and those who gave higher self-evaluations
tended to have higher salaries. The difference in salary
between two data scientists, one with a bargaining skill “1”
and the other with “5”, with otherwise identical demographics
and skills, is expected to be $10K–$15K.

Finally, in terms of work-life balance, our results show that
once you are working beyond 60 hours, salary estimates
actually go down.

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
problems to be solved with analytics (+1.5/+6.7), and communicating
with people outside the company (+5.4).

Time Spent in Meetings

People who spend more time in meetings tend to make more.
This is the variable we often use as a reminder that the model
does not guarantee that the relationships between significant
variables and salary are causative: if someone starts scheduling
many meetings (and doesn’t change anything else in their
workday) it is unlikely that this will lead to anything positive,
much less a raise.¹

Role of Coding

The highest median salaries belong to those who code 4–8
hours per week; the lowest to those who don’t code at all.
Notably, only 8% of the sample reported that they don’t
code at all, significantly down from last year’s 20%. Coding is
clearly an integral part of being a data scientist.

The Top Tools

The top two tools in the sample were Excel and SQL, both
with use by 69% of the sample, followed by R (57%) and
Python (54%). Compared to last year, Excel is up (from 59%),
as is R (from 52%), while SQL and Python are only slightly
higher than last year.

Over 90% of the sample reported spending at least some
time coding, and 80% used at least one of Python, R, and
Java, although only 8% used all three. The most commonly
used tools (except for operating systems) were included in
the model training data as individual coefficients; of these,
Python, JavaScript, and Excel had significant coefficients:
+4.6, –2.2 and –7.4, respectively. Less commonly used tools
were first grouped together into clusters and aggregate
features were included that represent counts of tools used from each cluster. For five clusters that were found to have a
significant correlation with salary, coefficients are added on a
per-tool basis.²

The cluster with the largest coefficient was centered on Spark
and Unix, contributing +3.9 per tool. Spark usage was 20%,
up from last year’s a modest 3%, and it continues to be used
by the more well paid individuals in the sample.

In contrast to the largely open source Spark/Unix cluster, the
second highest cluster coefficient (+2.4) was assigned to a
cluster dominated by proprietary software: Tableau, Teradata,
Netezza, Microstrategy, Aster Data, and Jaspersoft. In last
year’s report, Teradata also featured as a tool with a large,
positive coefficient. The other three clusters with significant
coefficients mostly consisted of open source data tools.

Which Tools to Add to Your Stack

While the model we’ve explained is a good way to get an estimate
for how much someone earns given a certain tool stack, it
doesn’t necessarily work as a good guide for which tool to learn
next. The real question is whether a tool is useful for getting
done what you need to get done. If you never have to analyze
more data than can fit into memory on your local machine, you
might not get any benefit—much less a salary boost—by using
a tool that leverages distributed systems, for example.

Salary and Sequences of Tools

In the following sequences of tools, the next tool in the
sequence was frequently used by respondents who used all
earlier tools, and these sequences had the best salary differentials
at each step.

If you know the first tool in a sequence, you might consider
learning the second, and so on.

Operating Systems

In our three previous Data Science Salary Survey reports, the
clearest division in tool clusters separated one group of open
source, usually GUI-less tools, from another consisting of
proprietary software, largely developed by Microsoft. Common
tools in the open source group have been Linux, Python,
Spark, Hadoop, and Java, and common tools in the Microsoft/
closed source group include Windows, Excel, Visual Basic, and
MS SQL Server. This same division appears when we cluster respondents, and is clearest when we look at the usage of
operating systems:

A set of tasks also emphasize the division between the first
two and last two clusters. The following percentages represent
respondents who indicated major engagement in these
tasks:

For all of the above tasks, the top two percentages were held
by clusters 3 or 4 and were both much higher than either
percentage for clusters 1 and 2.

Python, Matplotlib, Scikit-Learn

Another set of tools that exposed the primary split between
clusters 1/2 and 3/4 are Python and two of its popular
packages, Matplotlib (for visualization) and Scikit-Learn (for
machine learning):

Survey respondents assigned to clusters 3 and 4 tend to use
Python much more than those assigned to 1 and 2, and the
relative difference (as a ratio) grows when we look at the two
packages: cluster 3 and 4 respondents are 8–10 times as likely
to use them as cluster 1 and 2 respondents. Between clusters
3 and 4 there is a difference as well, albeit more minor:
cluster 3 has a higher Python usage rate, while a larger share
of cluster 4 respondents don’t use Python or these packages.
It turns out that these are the only tools whose highest usage
rate is among cluster 3 respondents.⁴ For most other tools
that are used much more frequently by clusters 3 and 4 than
by 1 and 2, they are also used more frequently by cluster 4
than by cluster 3.

Cluster 4 rates for two tasks also stand out:

Cluster 4, it seems, is much more of an “open source data
engineer” descriptor than cluster 3, which heads in that
direction but not nearly to the same extent. It’s not rare for
cluster 3 respondents to have used these tools—86% of them
used at least one—but on average they only used about 2.2.
In comparison, respondents in cluster 4 used an average of
5.3 tools. The fact that ETL and data management are much
more important in cluster 4 than cluster 3, implies that while
both might represent data science, cluster 3 tends toward the analyst’s side of the field, and cluster 4 tends toward the
engineering or architecture side.

As for the other two clusters, differences between clusters 1
and 2 become apparent once we look at the rest of the aforementioned
proprietary tool set. Cluster 2 respondents tended
to use these much more frequently.

For most of tools shown below, cluster 1 has the second
highest usage rate, but they significantly lag behind those of
cluster 2. Cluster 1 respondents tended to use fewer tools in
general: just under 8 on average, compared to 10, 13, and 21
for the three other clusters, respectively.

Tasks Without Coding

There are also some tasks that are undertaken by cluster 2
respondents significantly more frequently than those in other
clusters:

The first two tasks are functions of an analyst, and are
fairly common among cluster 3 and 4 respondents as well.
Crucially,
none of these tasks depend on being able to code
(at least, not as much as the four tasks above that are closely
associated with clusters 3 and 4). The low percentages for
cluster 1 sheds some light on the nature of this cluster: most
respondents in the sample whose primary function is not as a
data scientist, analyst, or manager seem to be grouped there.
This includes programmers who aren’t deep in the space (e.g.,
Java programmers who only use a few data tools). There are
analysts and data scientists in cluster 1, but they tend to have
small tool sets, and the composite feature of non-participation in many data tasks and non-use of data tools is what binds
cluster 1 together.

Some of the proprietary tools listed above are used by respondents
in cluster 4 about as much as those in cluster 1, most
notably SQL Server. In other words, they begin to violate the
primary cluster 1/2 vs. 3/4 split. A few other tools and tasks
take this pattern even further, or simply don’t show large
usage differences between clusters:

Tableau, Oracle, Teradata, and Oracle BI usage is higher in
clusters 2 and 4, lower in clusters 1 and 3. The same is true
for SQL, but like Excel and R, it’s exceptional in its wide usage
across all four clusters. In fact, SQL and Excel are the only two
tools (or tasks) that are used by over half of the respondents
in each cluster. R is not used as much by cluster 1, but usage
among the other three clusters is about the same: 67%–
69%. Data cleaning and basic exploratory analysis are similarly
high for clusters 2, 3, and 4, and much lower for cluster 1.
These tasks and tools cut across the cluster boundaries, and
don’t seem to have much correlation with the more salient
tool/task differences.

Managerial and Business Strategy Tasks

Perhaps even more illustrative of the connection between
clusters 2 and 4 are the managerial/business strategy tasks. The implication is that respondents in 2/4 tend to be more
senior, which turns out to be true, but only to an extent. In
terms of years of experience, clusters 1, 2, and 4 are about
the same—8–9 years on average—while for the cluster 3, the
average is much smaller: only 4.4 years; a similar difference
exists for age.

Despite representing the least experienced cohort, cluster 3
isn’t the lowest paid; that distinction goes to cluster 1, with
a median salary of $72K. At $84K, cluster 3 is still lower than
cluster 2 ($88K), but cluster 4 salaries tended to be far higher
than either, with a median of $112K. Cluster 4 respondents
tend to use a far greater number of tools than respondents
in the other clusters, and many of the tools they commonly
use are ones that had positive coefficients in the regression
model.