Data Science Skills To Boost Your Salary
Are you a data scientist wondering how your compensation stacks up to your peers? Or are you considering a career shift to data science? Here's a look at how much you can expect to earn.
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Data scientist may be the hottest job title in the IT and overall technology space right now. The number of data scientists has doubled in the last four years, according to a recent study of LinkedIn profiles performed by cloud analytics firm RJMetrics. Career site Glassdoor recently ranked data scientist as No. 1 on a list of top jobs that offer the best work-life balance. (You can see the rest of the list here.)
On Oct. 20, Glassdoor reported that it had 1,315 listings for data scientist job openings. According to Glassdoor, data scientists can expect a salary of about $115,000.
[Looking for more on skills to boost your career? Read 10 Skills CIOs Need to Survive, Thrive in 2016.]
So, if you are considering adding data science or analytics skills to your resume, you may find your value in the IT jobs market increase. If you are thinking about making a full career switch, you will be joining a small but fast-growing profession.
O'Reilly, which runs the Strata+Hadoop events, recently provided some additional data points for those considering a career in data science. The company published its third annual Data Science Salary Survey. The report covers compensation and also looks at trends in tools and job tasks for those in the data science field.
To compile the O'Reilly report, the authors used an online survey to collect information from more than 600 respondents who ran the gamut of job titles. Only about one-quarter of the participants had a job title that explicitly identified them as data scientists.
Others went by titles that included analyst, engineer, developer, architect, business intelligence professional, and statistician. Executives and management were also represented in the survey. Other participants were students, consultants, and professors. About two-thirds of those who participated are based in the US.
O'Reilly's authors ran several different models, as data scientists do, to look at the results in different ways. What follows are the results from a few different models, but we mostly used the results from the final model. This model excludes those who self-identified as upper management from some of the analysis, and also controlled for other factors. We recommend reading the full version of the report for more information.
O'Reilly notes that understanding salary is tricky:
Statistics from an anonymous online survey based on a self-selected sample doesn't exactly put the "science" into "data science," but such research can still be valuable -- and let's face it, much of the other information that might inform one's understanding of industry trends is in the same assumption-violating category.
Want to make this survey better? O'Reilly encourages data scientists to take the current survey. You can find it here.
But before you do that, take a look at the compensation, tools, and trends for data scientists in 2015. Once you've reviewed these findings, tell us what you think in the comments section below.
Are you currently working in a data science role? Does your compensation stack up? If you're not currently working in a data science-related position, is it something you're considering adding to your skill set in the year ahead?
**New deadline of Dec. 18, 2015** Be a part of the prestigious InformationWeek Elite 100! Time is running out to submit your company's application by Dec. 18, 2015. Go to our 2016 registration page: InformationWeek's Elite 100 list for 2016.
The median annual base salary for data scientists is $91,000 worldwide, and $104,000 for US respondents, according to the O'Reilly survey. This shows no significant change from last year's findings. Those in upper management (director, VP, or CxO) earn $32,003 more than the worldwide median.
Geography, gender, and advanced degrees all influenced the salaries of data scientists, according to the O'Reilly study. Here's how salaries for data scientists in these sub-groups differ from the worldwide median when controlled for other factors. For example, in the following list, women's salaries were controlled for all other factors, such as geography, advanced degrees, and negotiating skills:
Women earned about $2,007 less than the worldwide median salary, controlling for all other factors.
Those in California earned $13,200 more than the worldwide median.
Those in the Northeastern US earned $10,097 more than the worldwide median.
Those with a PhD earned $8,606 than the worldwide median.
Those with a master's degree but no PhD earned $851 more than the worldwide median.
The O'Reilly survey shows that the more time a data scientist spends in meetings, the more he or she earns. About half the respondents report spending at least one hour per day in a meeting. These respondents were paid $7,819 more than the worldwide median. The 12% of respondents who said they spend at least four hours per day in meetings were paid $9,036 more than the worldwide median.
Among the technical tasks data scientists perform, basic exploratory analysis accounts for one to three hours per day, according to 46% of respondents to the O'Reilly survey.
After this, data cleaning takes up the most time. Of the respondents, 39% spend at least one hour per day cleaning data.
The O'Reilly study also looked at negotiation skills and how these influence compensation. Participants were asked to rate their skills on a scale from one to five, with one being poor and five being excellent. Those who rated their bargaining skills as a five, or excellent, earn a salary that is $5,911 more than the worldwide median.
Here's the breakdown of what tools data scientists use, according to the O'Reilly survey:
SQL: 68% of respondents
Excel: 59% of respondents
Python: 51% of respondents
Usage of all of these tools remained flat compared with last year's survey.
The following tools showed the greatest increases in usage among data scientists surveyed this year compared to last year:
The number of respondents who said they use Spark increased 17% compared with the prior year, while the number who use Scala (the language in which Spark is written) increased 10%.
Last year, 25% of respondents said they use Tableau; this year 31% reported using it.
The following tools were among those showing the greatest decreases in usage among data scientists surveyed this year compared to last year:
R use fell from 57% of respondents to 52% compared to last year.
Java use was down from 32% of respondents last year to 23% this year.
Use of Apache Hadoop fell from 19% of respondents last year to 13% this year.
Perl use fell from 12% of respondents last year to 8% this year.
Matlab use declined from 12% of respondents last year to 6% this year.
Use of C# fell from 12% of respondents in 2014 to 6% this year.
Mahout use declined from 10% of respondents last year to 3% this year.
There are many, many other tools used by small percentages of data scientists.
Experience with cloud tools pays off. Those who do most or all of their work in the cloud earn $2,287 more than the worldwide median, according to the O'Reilly survey. Those who said they do no cloud computing at all earn $2,710 less than the worldwide median.
Data scientists who used certain tools in their work earned a bigger premium over the rest of the pack. Here are the top four tools that it pays to use, and how much more members of the sample earned, compared with the worldwide median.
Spark: $9,747
D3: $6,758
Amazon Elastic MapReduce: $4,878
Scala: $3,371
(Image Apache.org)
O'Reilly asked respondents how easy it was for them to find a new, comparable job. The answer, generally speaking, is pretty easy. Respondents were asked to rate the difficulty of finding a comparable new job on a scale of from one to five, with one being very difficult and five being very easy. Here's how it breaks down, by percentage of respondents:
Very easy: 23%
Easy: 35%
Medium: 29%
Difficult: 8%
Very difficult: 5%
Those who rated finding a new job very easy also had the highest median worldwide salary, at $112,000.
The top four tools used by those who said it was easy to find a comparable position were Redshift, Teradata, Amazon EMR, and Cloudera. The bottom four tools cited by this group were SPSS, C#, Perl, and BusinessObjects.
O'Reilly asked respondents how easy it was for them to find a new, comparable job. The answer, generally speaking, is pretty easy. Respondents were asked to rate the difficulty of finding a comparable new job on a scale of from one to five, with one being very difficult and five being very easy. Here's how it breaks down, by percentage of respondents:
Very easy: 23%
Easy: 35%
Medium: 29%
Difficult: 8%
Very difficult: 5%
Those who rated finding a new job very easy also had the highest median worldwide salary, at $112,000.
The top four tools used by those who said it was easy to find a comparable position were Redshift, Teradata, Amazon EMR, and Cloudera. The bottom four tools cited by this group were SPSS, C#, Perl, and BusinessObjects.
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