Top Skills and Backgrounds of Data Scientists on LinkedIn

Posted on November 8, 2015 by GilPress

A new study of LinkedIn profiles by RJMetrics has found that the number of data scientists has doubled over the last 4 years . This reflects the increasing demand for sophisticated data analysis skills, combining computer programming with statistics, and the growth in the popularity of the term “data science” both in job openings and the words people use to describe their work on LinkedIn. At least 52% of all current 11,400 data scientists on LinkedIn have added that title to their profiles within the past 4 years.

Cumulative Number of Data Scientists Over Time_RJMetrics

In the chart above, the cumulative number of data scientists in any given year corresponds to the number of present-day data scientists who started their first job that year. We can safely assume that those who started their first jobs between 1995 and 2009 were not called then “data scientists,” but the data shows the cumulative growth in the number of professionals who have this title today.

Here are the other highlights of the study:

The high-tech industry (LinkedIn classification: Information Technology and Services industry, Internet and Computer Software industries) employs 44.9% of the professionals identified on LinkedIn as data scientists, followed by education (8.3%, probably employed mostly by universities), Banking and Financial Services (7.2%), and Marketing and Advertising (5.2%).

The top ten companies employing data scientists are Microsoft, Facebook, IBM, GlaxoSmithKline, Booz Allen Hamilton, Nielsen, GE, Apple, LinkedIn, and Teradata. Note that Google is not at the top ten, possibly because the data science Googlers on LinkedIn adhere to the title Google bestows on them: quantitative analyst.

Data Scientists Per Company_RJMetrics

Both Microsoft and Facebook, according to RJMetrics’ analysis, appear to be on a hiring spree, accelerating their data scientist recruiting during the 2014 calendar year by at least 151% and 39%, respectively, when compared to 2013. But given the scarcity of experienced data scientists, it’s a revolving door, with Microsoft also losing the largest number of data scientists over that period.

So how do you become one of these unicorn data scientists, commanding annual salaries of $200,000 plus? The study provides fresh data on the skills and background of data scientists.

RJMetrics analyzed 254,000 skill records of the data scientists on LinkedIn and ranked each skill by the number of people listing it on their profile. In addition to the catch-all categories of “data analysis,” “data mining,” and “analytics,” the top skills are R, Python, machine learning, statistics, SQL, MATLAB, Java, statistical modeling, and C++. Hadoop (20.9%) is at the bottom of the top 20, as a specific skill, behind SAS (22.78%).

Top 20 Skills of A Data Scientist_RJMetrics

An analysis of skills by job levels revealed that chief data scientists appear to be less technical on average: Only 27% and 26% listed Python and R, respectively, compared to 52% and 53% of junior data scientists, along with 38% and 43% of senior practitioners. Those at higher level jobs may not need to emphasize their technical skills or may not need them in positions where management experience and knowledge of a business domain are valued more than technical proficiency.

Over 79% of data scientists listing their education have earned a graduate degree, with 38% of all data scientists who had an education record earning a PhD, and close to 42% listing a Master’s degree as the highest degree attained.

Computer Science is the dominant field of study among data scientists, followed by business administration/management, statistics, mathematics, and physics. Only 4.6% of data scientists list “machine learning/data science” as their graduate degree, a number that will probably increase in coming years due to the proliferation of new Master in Data Science programs, supplanting the older Master in Analytics programs.

Top 20 Backgrounds of Data Scientists with a Graduate Degree_RJMetrics

Note that RJMetrics included in their sample only data scientists associated with specific companies, assuming that those listing “data scientist” in their profile without an association with an actual company may only have aspirations about a career in data science, but not actual experience. They analyzed 60,200 records of professional experiences, 27,700 records of education, and 254,600 records of skills, and information about 6,200 unique companies that employed self-identified data scientists as of June 1, 2015.

For other recent studies of the skills and salaries of data scientists see here and here.

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2 New Surveys About the Market for Data Scientists

Posted on May 17, 2015 by GilPress

Two new surveys tell us a lot about both the supply and demand sides of the hot market for data scientists, “the sexiest job of the 21st Century.”

On the demand side—the challenges of recruiting, training, and integrating data scientists—we have the MIT Sloan Management Review and SAS fifth annual survey of 2,719 business executives, managers and analytics professionals worldwide. On the supply side—the talent available and what salaries it commands—we have the second annual Burtch Works Study, surveying 371 data scientists in the U.S. (see also the video presentation at the end of this post).

The median salary of a junior level data scientist is $91,000, but those managing a team of ten or more data scientists earn base salaries of well over $250,000, according to Burtch Works. Supply is still tight and top managers enjoyed over the last year an eight percent increase in base salary and median bonuses over $56,000. When changing jobs, data scientists see a 16 percent increase in their median base salary.

Who are these data scientists that are so much in demand? The vast majority have at least a master’s degree and probably a Ph.D., and one in three are foreign-born. But with a younger generation of data scientists, freshly minted from more than 100 graduate programs worldwide, the median years of experience dropped from 9 in 2014 to 6 in 2015.

As data science is increasingly adopted by all companies in all industries, the proportion of data scientists employed by startups—the firms that have dominated the application of big data analytics— declined from 29 percent in 2014 to 14 percent in 2015.

It is the mainstreaming of data science and the specific challenges of acquiring and benefiting from this still-scarce talent pool that is the focus of the MIT Sloan Management Review survey. Four in ten (43%) companies report their lack of appropriate analytical skills as a key challenge but only one in five organizations has changed its approach to attracting and retaining analytics talent.

As a result of the scarcity of data scientists, 63 percent of the companies surveyed are providing formal or on-the-job training in-house. “One big plus of developing analytics skills among current employees,” says the report, “is that they already know the business.” These companies are also doing more to train existing managers to become more analytical (49%) and train their new data scientists to better understand their business (34%). Still, half of the survey respondents cited turning analytical insights into business actions as one of their top analytics challenges.

To better manage these challenges, the study recommends giving preference to people with analytical skills when hiring and promoting, developing analytical skills through formal in-house training, and integrating new talent with more traditional data workers.

“Infusing new analytics talent without proper support and guidance can alienate traditional data workers and undermine everyone’s contributions,” says the report. Yet only 27% of companies report that they successfully integrate new analytics talent with more traditional data workers. So even after managing to find (and pay for) the data science talent, there is no guarantee for the desired results, either because of the lack of understanding of the business by the new recruits, resistance from current employees engaged in data preparation and analysis, or failure to translate new insights into meaningful action.

Many companies have responded to these challenges by creating new roles and responsibilities and devising new organizational structures. The report points out that the range of analytics skills, roles and titles within organizations has broadened in recent years. What’s more, new executive roles, such as chief data officers, chief analytics officers and chief medical information officers, have emerged to ensure that analytical insights can be applied to strategic business issues.

Whether the work is centralized or decentralized, data science and analytics should be perceived and managed by companies as a professional function with its own clear career path and well-defined roles. Tom Davenport asked in a recent essay: “When was the last time you saw a job posting for a ‘light quant’ or an ‘analytical translator’? But almost every organization would be more successful with analytics and big data if it employed some of these folks.”

Davenport defines a “light quant” as someone who knows something about analytical and data management methods, and a lot about specific business problems, and can connect the two. An “analytical translator” is someone who is extremely skilled at communicating the results of quantitative analyses.

Data science is a team sport that requires the right blending of people with different skills, expertise, and experiences. Data science itself is an emerging discipline, drawing people with diverse educational backgrounds and work experiences. Typical of the requirements for a graduate degree is what we find in a recent announcement from the University of Wisconsin’s first system-wide online master’s degree in data science: “The Master of Science in Data Science program is intended for students with a bachelor’s degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst, information technology analyst, database administrator, computer programmer, statistician, or other related position.”

As with any team sport, there are stars that are paid more than the average player. According to Glassdoor (HT: Illinois Institute of Technology Master of Data Science program), the average salary for data scientists is a bit more than what Burtch Works reported, at over $118,000 per year. (By the way, Glassdoor reports the average salary for statistician is $75,000 and $92,000 for a senior statistician).

It’s possible that the Glassdoor numbers include more of what Burtch Works calls “elite data scientists.” Do we know who is in the elite of top data science players? The closest we get to identify the MVP of data science is the Kaggle ranking of the data scientists participating in its competitions. Currently, Owen Zhang is number one. Zhang says on his profile that “the answer is 42” and his bio section tells us that he is “trying to find the right question to ask.” He lists his skills as “Excessive Effort, Luck, and Other People’s Code.”

Zhang is currently the Chief Product Officer at DataRobot, a startup helping other data scientists build better predictive models in the cloud. He is also yet another example of how experience and skills still matter today more than formal data science education. His educational background? Master of Applied Science in Electrical Engineering from the University of Toronto.

This Burtch Works webinar provides highlights from the 40+ pages of compensation and demographic data in the report, which is available for free download here: http://goo.gl/RQX1xd

[youtube https://www.youtube.com/watch?v=aEkpVr8Q6oI?rel=0]

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Data Scientists Still Hot, Salaries Cool Off

Posted on May 28, 2016 by GilPress
Burtch16_Figure 5
Burtch16_Figure 6

The third annual Burtch Works Study: Salaries of Data Scientists April 2016 is out, documenting the continuation of a very favorable market for those with the sexiest job of the 21st century.  However, the salaries of data scientists appear to be leveling off: Every job category except one (entry-level individual contributors) experienced a marginal single-digit shift in median base salary over the past year. This compared to the overall increase in compensation of 14% in last year’s report.

The Burtch Works Study is based on compensation and demographic data for 374 data scientists collected in interviews conducted by Burtch’s recruiting staff during the 12 months ending March 2016. It focuses on data scientists as distinguished from other analytics professionals, defining them as follows:

Data scientists apply sophisticated quantitative and computer science skills to both structure and analyze massive unstructured datasets or continuously streaming data, with the intent to derive insights and prescribe action. The depth and breadth of their coding skills distinguishes them from other predictive analytics professionals and allows them to exploit data regardless of its source, size, or format. Through the use of one or more general-purpose coding languages and data infrastructures, data scientists can tackle problems made very difficult by the size and disorganization of the data.

 

Here are the highlights of the new report.

Individual contributors: Median base salaries range from $97,000 at level 1 to $152,000 at level 3 plus bonuses ranging from $10,000 to $21,000 (over 73% of all individual contributors are eligible for bonuses).

Managers: Median base salaries range from $140,000 at level 1 to $240,000 at level 3 plus bonuses ranging from $15,000 to $80,000 (over 80% of managers are eligible for bonuses).

Salary changes from last year’s study: Base salaries for individual contributors have increased 7% at level 1 and 1% at level 3, while salaries remained steady at level 2. For managers, salaries remained steady at level 1 while those at level 2 increased 3%. At level 3, the median base salary decreased by 4% ($10,000).

Data scientists continue to get top compensation for analytics professionals: Data scientists earn base salaries up to 39% higher than other predictive analytics professionals depending on job category.

Burtch16_Figure 9.jpg

A shift in the educational background of data scientists: 59% of level 1 individual contributors’ highest degree is a Master’s, a significant increase from last year’s 48%.

An increase in the number of U.S. citizens in the data science talent pool: Among level 1 individual contributors, only 43% of this year’s professionals are foreign-born vs. 53% last year.

It appears that the increase in the number of graduate-level programs in data science has started to make its mark and is contributing to an increase in the supply of entry-level data scientists with a Master’s degree. Other trends Burtch Works has observed in its recent conversations with data scientists are increased desire to work for “more mission-driven organizations attempting to make an impact on society” rather than large companies such as Facebook or Google and “the increasing pressure on many startups to show their value,” otherwise known as the coming burst of the Unicorn Bubble.

If we do see a contraction in startup activity and attractiveness over the next year, it may well be that larger and more stable companies, even in traditional industries, will become more desirable for budding—and even experienced—data scientists, regardless of their desire to “change the world.” The job opportunities—and the high compensation—will certainly be there as the practice of data science spreads into all corners of the economy. As Burtch Works predicts: “The use of data science will become more ubiquitous, the talent supply will improve, and there will be even more use cases for these techniques.”

Originally published on Forbes.com

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Current Salaries for AI Professionals and Data Scientists

Posted on June 15, 2022 by GilPress

A new salary survey of AI professionals and data scientists finds unprecedented annual increase in compensation for data analysis skills and experience.

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NewVantage Partners Survey: Big Data Now Mainstream, Term Still Widely Disliked

Posted on December 1, 2014 by GilPress

For the third year in a row, NewVantage Partners has conducted a survey of Fortune 1000 senior business and technology executives regarding their companies’ investments in big data. This annual survey (see my summary of the 2013 report here) provides a unique glimpse into the big data initiatives of large enterprises and how fast they have adopted the set of technologies, processes, and skills associated with it.

BD_NV14_Figure 5Big data has gone mainstream with 67% of executives reporting that they have big data initiatives running in production, up from 32% last year. A remarkably rapid adoption rate, since only two years ago 85% of the respondents had a big data initiative “planned or in progress.” While a third of respondents in 2012 had big data investments of $1 million or more, 45% reported big data spending of greater than $10 million this year, and 74% estimated that their companies will spend that amount in 2017.  82% of the executives surveyed said big data is already integrated into the mainstream of their organizations while only 12% said it is managed as a separate environment.

Who leads this new mainstream corporate activity which 82% of the executives surveyed view as being highly important or mission-critical within their organizations? 37% of executives reported that the primary executive sponsor is the CEO, COO, or business President, stressing the importance of sponsorship from the top of the organization. And who does the executive sponsor turn to for big data accountability? Leading the charge in many situations is the Chief Data officer (CDO) with 43% of executives reporting that their organization has defined and established such a role. This is a sharp jump from previous years–in 2012 only 19% of executives reported that their organization had established a CDO function, growing only slightly to 26% in 2013.

The business function that is the primary driver of investment in big data for 36% of the respondents is sales and marketing. But other functions also lead big data initiatives, including those focused on internal activities: Risk, security, regulatory, and compliance (29%); R&D and new products (21%); IT and operations (10%); and finance, HR and administrative (4%).

What are these diverse functions expecting from their investments in big data?  Three quarters of executives cited greater insight and learning, the ability to answer business questions faster, make informed decisions faster, and accelerate speed-to-market as the primary business driver for their big data initiatives.BD_NV14_Figure 13

“It should be noted,” the NewVantage Partners report state, “that the level of skepticism [about big data] has dropped significantly since we first conducted this survey in 2012.” Indeed, most of the executives surveyed (74%) now believe that big data warrants “serious attention” and only 3% call it “same old stuff.” But the level of comfort with the term itself is still very low. Most respondents said that the term is not helpful in focusing a serious discussion about the value and benefits of big data to their organizations and 83% of executives claim to dislike the term big data (53%), or find it to be overstated (30%).

BD_NV14_Figure 12aAs Tom Davenport writes in his introduction to the report: “Everything is sunny in the world of big data… except, its name! Perhaps never has there been such enthusiasm about a business topic, and less satisfaction with the name of it… [Big data is] clearly not just hype, and it’s not just experimental. Companies are getting real value from it in production applications. Now if we could only figure out what to call it!”

It is clear, however, that the executives answering the survey, while not happy with the term itself, knew quite well what they were talking about when they referred to their big data investments. For them, it is a new type of activity associated with new programs and new roles and responsibilities, and it is centered on new business drivers (see above), but also a diverse set of needs related to the rise in the volume and variety of data. When asked about the most important technical driver for their big data investments, they answered as follows: Integrate more varieties of data (22%); apply new big data approaches (17%); integrate larger volumes of data (16%); more effectively integrate legacy data (13%); and enable greater agility (12%).

Regardless of the mainstream coverage big data gets which is mostly about the new technologies (e.g., Hadoop) it is associated with, its ascendance to the mainstream of IT investments by large enterprises has much more to do with the success of these investments in answering specific business needs and demonstrating the value of improved data analysis.

[Originally published on Forbes.com]

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Big Data Market 2011-2026, From $7.6 to $84.69 Billion

Posted on May 14, 2015 by GilPress

BigData_Wikibon2015

Wikibon: For the calendar year 2014, the Big Data market – as measured by revenue associated with the sale of Big Data-related hardware, software and professional services – reached $27.36 billion, up from $19.6 billion in 2013. While growing significantly faster than other enterprise IT markets, the Big Data market’s overall growth rate slowed year-over-year from 60% in 2013 to 40% in 2014. This is to be expected in an emerging but quickly maturing market such as Big Data, and Wikibon does not believe this slightly slower growth rate indicates any structural market issues.

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Jeff Kelly and Dave Vellante from Wikibon on the Big Data Market (Video)

Posted on February 26, 2014 by GilPress

[youtube http://www.youtube.com/watch?v=ef_yQvtC9aA?list=PLenh213llmcYiyiYRzkku1MgwvrL_TIGN]
From Silicon Angle:

Our coverage kicked into high gear after the release of Wikibon’s third annual Big Data Vendor Revenue and Market Forecast, which author Jeff Kelly stopped by to discuss with hosts John Furrier and Dave Vellante.

Companies spent $18.6 billion on analytics in 2013, according to the report, up 58 percent over the previous 12 months and about two and a half times more than in 2011. Kelly estimates that the industry will pass the $28 billion mark by the end of this year and achieve revenues of over $50 billion in 2017, a massive increase he credits to rapidly growing demand for emerging solutions such as NoSQL databases as well as more established technologies that are proving valuable in extracting insights.

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Jake Flomenberg from Accel Partners on the Big Data Market (Video)

Posted on February 26, 2014 by GilPress

[youtube http://www.youtube.com/watch?v=SHOw-2IWHZE]

From VentureBeat:

In a new video, Jake Flomenberg of Accel Partners lays out his view of the big data market and the investing opportunities he’s excited about. He’s talking with another data expert: Stefan Groschupf, the chief executive of well-funded big data startup Datameer.

Flomenberg knows what he’s talking about: He worked on sales, marketing, and product problems at hot data startup (and likely IPO candidate) Cloudera.

He’s one person who works with Accel’s big data fund. He managed to get in on hot data-transformation startup Trifacta, as well as marketing-focused Origami Logic and log-management company Sumo Logic.

 

 

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The Data Market to Nearly Double in Size by 2019

Posted on May 28, 2015 by GilPress

DataMarket

Consisting of data platforms, data management, analytics, and data mining the Total Data Market is expected to nearly double in size, from $60bn in 2014 to $115bn in 2019. The forecast is based on 451 Research’s new Total Data Market Monitor service, which presents data, generated via a bottom-up analysis, of 202 vendors that participate across the nine Total Data segments the company tracks.  Specifically, 451 Research tracks 56 Operational Database participants, 26 in the Analytic Database market, 72 within the Reporting and Analytics segment, 41 Data Management vendors, 11 Performance Management vendors, 11 Event/Stream Processing vendors, 9 Distributed Data Grid/Cache vendors, 25 Hadoop vendors and 15 Search vendors.

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Big Data Landscape 2017: Big Data + AI = New IT Stack

Posted on April 11, 2017 by GilPress

Big-Data-Landscape-2017-Matt-Turck-FirstMark.png

Matt Turck:

We’re witnessing the emergence of a new stack, where Big Data technologies are used to handle core data engineering challenges, and machine learning is used to extract value from the data (in the form of analytical insights, or actions).

In other words: Big Data provides the pipes, and AI provides the smarts.

Of course, this symbiotic relationship has existed for years, but its implementation was only available to a privileged few.

The democratization of those technologies has now started in earnest.  “Big Data + AI” is becoming the default stack upon which many modern applications (whether targeting consumers or enterprise) are being built.  Both startups and some Fortune 1000 companies are leveraging this new stack…

Often, but not always, the cloud is the third leg of the stool. This trend is precipitated by all the efforts of the cloud giants, who are now in an open war to provide access to a machine learning cloud.

 

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