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Business Intelligence Analyst Salary Kansas City – O’Reilly released the results of its salary survey this week, which showed that the average salary for AI and data professionals in the US and UK is $146,000 a year. While that’s certainly more than many jobs, it represents just a 2.25% annual growth rate on average, according to the survey, which also broke down salaries by education, job title, gender, state, programming language and platform.
Among the 3,136 data and AI professionals (i.e., data scientists, data engineers, and AI and machine learning specialists) surveyed by O’Reilly online in June, those from California earned the most, earning an average of $176, 000 per year. That shouldn’t be surprising considering the number of major tech firms that make their home in Silicon Valley, as well as the state’s overall high cost of living. The Golden State was followed by Massachusetts, Delaware, New Jersey, New York and Maryland with median salaries below $150,000.
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Males made up 81% of survey respondents and reported median salaries ($150,000) that were significantly higher than female salaries ($126,000). Women earned just 84% of what men did, the survey said. “This difference holds regardless of education,” O’Reilly points out in the survey.
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Despite the pay gap, however, a higher percentage of women had higher education than men, according to the survey, which found that 16% of women had a doctorate compared to 13% of men. And 47% of women had a master’s degree, compared to 46% of men.”
Differences in salaries between the sexes were also noted when looking through the optics of the job position. “At the executive level, the average salary for women was $163,000 compared to $205,000 for men (a 20% difference),” O’Reilly says in the report. “At the director level, the gap was much smaller — $180,000 for women versus $184,000 for men — and women’s salaries [at the director level] were actually higher than executive-level salaries.
O’Reilly also collected data on what programming languages AI and data professionals use. Unsurprisingly, the usual suspects were always present: Python (used by 61% of respondents), SQL (54%), JavaScript 32%), Bash (29%), Java (24%) and R (20%) .
But when the Sebastopol, Calif.-based media company compared language data with salary data, some interesting trends emerged. For example, AI and data professionals who ticked Rust had the highest average salary ($180,000+) of any language. It was followed by Go ($179,000) and Scala ($178,000).
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O’Reilly explained it this way: “The talent pool for newer languages like Rust and Go is relatively small. While there may not be much demand for data scientists using these languages (yet), there is clearly some demand – and with a shortage of experienced Go and Rust programmers, they demand higher pay,” the report said.
Unsurprisingly, the most popular languages (Python, SQL, Bash, and R) regressed to the mean in terms of salary, with the group in this study making just under $150,000, which is right around the absolute median salary. Java has shown slightly increased traction, averaging just over $150,000 (just ahead of everyone’s favorite, “I don’t use programming languages”). Negative values included Perl, D, and CSS, which were valued at around $125,000 per year. level.
O’Reilly also asked what tools and platforms AI and data professionals use for statistics and machine learning, and this is where the salary comparisons get really interesting. For starters, it’s obvious that the people who use these tools are paid quite a bit more than the average respondent in this survey; that is, the distribution is greater than $150,000.
Unsurprisingly, some of the most popular statistics and machine tools and platforms, such as PyTorch and TensorFlow, are clustered in the middle, which is around $155,000 in this chart. Higher-end outliers include H2O.ai, for which the average user is paid around $175,000, and KNIME just below that. The first five are rounded off by Spark NLP, SparkMLlib and Google Prediction.
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The lowest is Stata, with an average salary of around $115,000 per year. Then there’s the big jump to IBM System ML and Nimble (a lightweight deep learning library based on PyTorch) at around $140,000, closely followed by Excel and everyone’s favorite ML/stats package “I don’t use any ML/stats tools” right around median for the entire survey (approximately $145,000).
The salary data around the data frames was also interesting. Clicktale, a cloud-based analytics service that ContentSquare acquired in 2019, led the pack with an average salary of $225,000, followed closely by Tecton, a cloud-based features business we wrote about earlier this year.
Data and AI professionals who reported using Ray, Amundsen, and Apache Kafka had higher-than-average salaries of $160,000 to $180,000. The bottom man on the framework totem pole was Google Analytics at just under $150,000, showing that like those who reported using statistical/ML tools, people using these frameworks are likely to be senior data scientists/ML engineers and are more rewarded.
The last piece of interesting data concerns training. People who reported more than 100 hours of training reported an average salary increase of more than $11,000. O’Reilly says respondents who took advantage of company-provided training — including certification programs, a technology conference or other type of training — had the highest average salaries. The data was similar for those data and AI professionals who earned certifications.
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“Our survey reveals the extent to which data and AI professionals are committed to advancing their careers through skills development and training,” Mike Loukides, report author and vice president of content at O’Reilly, said in a press release. “Getting the right learning and development right for companies is critical to retaining and attracting top talent in this hot job market.
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