Data and artificial intelligence professionals are intent on learning new skills and advancing their careers. However, in many cases, organizations are failing to compensate them accordingly, and as such, are having a hard time retaining technical staff from an already slim talent pool.
To explore the link between training and compensation, learning platform O’Reilly issued a new Data/AI Salary Survey. The findings showed that 91% of respondents (largely tech professionals) were interested in learning new skills or improving upon existing skills, and 64% took part in training or received certifications during the past year.
Furthermore, a majority of those who participated in training (61%) signified that they did so with the expectation of a raise or promotion. But the average increase in compensation over the last three years only amounted to a 2.25% increase annually.
The Value of New Skills
Despite marginal salary increases on average, it’s not all grim news. The survey found a clear correlation between the amount of training one receives and an increase in pay, underscoring the importance of learning on the job. In fact, those who spent more than 100 hours on training saw an average salary increase of $11,000, while those who invested less than 20 hours in training saw an average increase of only $7,100.
Additionally, 56% of respondents cited improving job security as a motivation for training. This is likely a byproduct of uncertainty brought on by the pandemic. Despite this, only 9% said they took training out of fear of losing their job. While 22% of respondents said they were looking to change jobs because their salaries hadn’t changed over the past year, that rate is expected in a field where employees tend to change jobs every three or four years.
Salary Considerations: Location, Gender, and Certifications
Overall, data and AI professionals are well-paid, with an average annual salary of $146,000. But there are fairly significant regional fluctuations, with the highest salaries hailing from coastal states. California led the pack with an average salary of $176,000, followed by Eastern Seaboard states like New York and Massachusetts. The majority of salaries averaged between $100,000 and $150,000.
While differences in pay by location are to be expected, unfortunately, so too are salaries by gender. Women, who made up 14% of survey respondents, are paid significantly less than their male counterparts, making only 84% of what their male colleagues bring home.
The pay gap was consistent regardless of job title or education, with compensation for women executives averaging $163,000 versus $205,000 for men.
The average salary for a woman with a doctorate or master’s degree was 82% of that for a man with an equivalent degree, even though a higher percentage of women had doctorates than men (16% vs 13%), as well as master’s degrees (47% vs 46%).
In addition to location and gender, certifications and competency in certain programming languages also played a part in compensation. The types of certifications that respondents had were spread across specialties, but cloud certifications were the most popular with AWS and Azure leading the way. Achieving those types of certifications also resulted in higher salary increases.
Up Ahead: Machine Learning and the Cloud
Looking ahead, machine learning will continue to be a hot topic. In fact, 63% of respondents indicated that machine learning will have a direct impact on employee salary and promotion evaluations. Cloud-related skills also will be in demand, with cloud and containers (47%), data tools (46%), and automation (44%) rounding out the list of desired skills.
Surprisingly, programming languages came in near the bottom of the list, cited by only 34% of the respondents. Obviously, programming language skills will continue to be important, but despite employers’ willingness to pay for skills in certain programs, employees may not see these as standout skills that can lead to a promotion or pay increase.
In general, the employment and salary landscape among data and AI professionals is solid, with variations in salary increases and job movement within expected ranges. Respondents are focused on improving their skills, partly out of a desire to get better at their jobs and also to help to increase their pay. Although there is some concern about job security, likely a result of pandemic-induced uncertainties, those concerns don’t seem to be driving too many decisions about employment.