Data and AI wages continue to rise in March, says O’Reilly
O’Reilly this week released the results of his salary survey, which showed the average salary for AI and data professionals in the US and UK to be $ 146,000 per year. While that is certainly more than many jobs, it only represents an annual rate of increase of 2.25% on average, according to the survey, which also divided salaries based on education, title position, gender, status, programming language and platform.
Among the 3,136 data and AI professionals (v. This shouldn’t be surprising, given the number of large tech companies that have made Silicon Valley their home, as well as the high overall cost of living of the State Massachusetts, Delaware, New Jersey, New York and Maryland followed the Golden State, with average salaries below $ 150,000.
Men made up 81% of those surveyed and reported average salaries ($ 150,000) significantly higher than those of women ($ 126,000). Women earned only 84% of what men earned, according to the survey. “This differential was maintained regardless of education,” said O’Reilly in the survey.
However, despite the pay gap, a higher percentage of women than men had higher degrees, according to the survey, which shows 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.
Gender pay differences were also seen through the prism of job title. “At the executive level, the average salary for women was $ 163,000 versus $ 205,000 for men (a 20% difference),” says O’Reilly in the report. “At the manager level, the difference was much smaller – $ 180,000 for women versus $ 184,000 for men – and women’s salaries. [at the director level] were in fact higher than those at the executive level.
But when the Sebastopol, California-based media company crossed the linguistic data with the salary data, some interesting trends emerged. For example, the AI and data professionals who checked Rust had the highest average salary ($ 180,000 +) of any language. Next are Go ($ 179,000) and Scala ($ 178,000).
O’Reilly tried to explain it: “The talent supply for newer languages like Rust and Go is relatively small. While there may not (yet) be a huge demand for data scientists using these languages, there is clearly some demand – and with experienced Go and Rust programmers in short supply, they are ordering a higher salary, ”he said in the report.
Unsurprisingly, the most popular languages (Python, SQL, Bash, and R) have returned to par when it comes to salaries, with a group of them posting just under $ 150,000, just around the median salary. absolute in this study. Java showed slightly high traction, averaging just over $ 150,000 (just ahead of everyone’s favorite language, “I don’t use programming languages”). The outliers on the downside included Perl, D and CSS, which clustered around the $ 125,000 / year. level.
O’Reilly also asked what tools and platforms AI and data professionals use for statistics and machine learning, and this is where salary comparisons get really interesting. For starters, it is obvious that the people who use these tools are paid slightly higher than the average respondent to this survey; that is, the distribution is greater than $ 150,000.
Unsurprisingly, some of the most popular stats and machine tools and platforms, such as PyTorch and TensorFlow, are clustered in the middle, which is roughly $ 155,000 on this chart. High-end outliers include H2O.ai, for which the average user is paid around $ 175,000, and KNIME, just below. Spark NLP, SparkMLlib, and Google Prediction round out the top five.
The outliers at the bottom of the scale are Stata, with an average salary of around $ 115,000 per year. Then there’s a 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 / stat package. ”I does not use any ML / statistics tool “all around the median for the entire survey (about $ 145,000).
The salary data around the data frames was also interesting. Clicktale, a cloud-based analytics service that was acquired by ContentSquare in 2019, led the pack with an average salary of $ 225,000, closely followed by Tecton, a cloud-based features store of which we have talked about earlier this year.
Data and AI professionals who reported using Ray, Amundsen, and Apache Kafka had above-average salaries, ranging from $ 160,000 to $ 180,000. The lowest man on the framework totem pole was Google Analytics, with a salary of just under $ 150,000, showing that, like those who reported using statistics / ML tools, people who use these frameworks are probably more experienced data scientists / ML engineers and are better paid.
The last interesting data concerns training. People who reported more than 100 hours of training reported an average pay increase of more than $ 11,000. O’Reilly says respondents who took advantage of training offered by the company, including certification programs, a technical conference or some other type of training, had the highest average salaries. The data was similar for this data and the AI professionals who achieved certifications.
“Our survey reveals the extent to which data and AI professionals are dedicated to advancing their careers through skills development and training,” said Mike Loukides, author of the report and vice president of content at O’Reilly, in a press release. “Get L&D [learning and development] law is crucial for companies to retain and attract the best talent in this fast-paced job market.
You can read more about the O’Reilly 2021 Data / AI Salary survey and download the report at www.oreilly.com/radar/2021-data-ai-salary-survey.
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