Updated Job Change & Tenure Data for Data Scientists & AI Professionals
This blog is contributed by Burtch Works’ Data Science recruiting team.Having current data on how many research & insights professionals are changing jobs, as well as how long they’re staying at those jobs, can be informative for both employers and professionals working in the field.While recent events like the ‘Great Resignation’ will undoubtedly have an impact on the 2022 hiring market going forward (we’ve written more about the trends we’re already seeing here), we wanted to publish our annual job change research so that the data is available to those who need it.
Average Tenure and Job Change Data for Data Scientists & AI Professionals
Each year we examine a sample of hundreds of professionals in our network to determine:
- How many had changed jobs in the previous year (2021)?
- And, among those who had changed jobs, how long did they stay in their previous position?
For this research we always focus on professionals who had joined a new company for their job change, and do not include those who received a promotion or made a transition within the same company.
33% of professionals changed jobs in 2022 compared to 12.4% in 2017, and we found that their average tenure at their previous role was about 3.2 years. These figures do not include professionals who received a promotion or stayed within the same company.
As we’ve found in the past, professionals who are earlier in their careers tended to change jobs more frequently and held shorter tenure than those who were in the later stages of their careers. For example, among data scientists and AI Professionals with 0-5 years’ experience, 45% changed jobs and their average tenure in their previous position was 2.4 years. For professionals with 6-10 years’ experience, we found 52% changed jobs and their average tenure was 3.1 years in their previous position.Here at Burtch Works we often advise early career professionals to take advantage of opportunities to gain exposure to many different research tools, skills, and methodologies. This can provide a valuable base of experience from which to draw and will be especially helpful as you move into new industries, categories, or research specialties down the road. Industry disruption can be difficult to predict, which is why it’s so important to develop a range of experience in different areas early on to build a strong foundation that can be adaptable moving forward.
What does this mean?
The effects of the COVID-19 pandemic on the hiring market have shifted significantly over the past few years. During the immense disruption of 2020, many professionals chose to put planned job searches on hold, while some data science teams were more impacted than others in terms of their hiring plans and response to the initial crisis. As the economic recovery picked up in 2021, we saw more data science teams planning to hire. There was also massive turnover in the labor market, due to the Great Resignation, as many professionals resumed their job searches or sought to change their working situation due to other pandemic related factors.Unfortunately for those looking to hire, this has led to fewer professionals on the market in 2022, and a lower inventory of available candidates. Many candidates that are still on the market are coming to the table with numerous offers and are seeking a meaningful increase in salary, along with other benefits.Recently, large tech companies have made headlines announcing hiring freezes and potential layoffs in 2023. We will continue to monitor the situation as it evolves and will be sure to share our findings!