While the results of our recent quantitative hiring flash survey showed many teams are looking to hire data scientists and analytics professionals, we’re always curious to look at other measures of market activity!
While employers may look to these numbers to evaluate recruiting and retention strategy, for data scientists and analytics professionals, it can also be beneficial if you’re, say, considering a career move (or proactively considering your career strategy) and wondering where you stand in regards to the industry average tenure.
Average Tenure and Job Change Percentages for Data Scientists & Analytics Professionals
To gauge the activity of the hiring market for data scientists and analytics professionals, we looked at a sample of hundreds of quantitative professionals in our network to determine:
- How many had changed jobs in 2018?
- And among those who had changed jobs, how long had they stayed in their previous position?
For this research we focused on quantitative professionals who had actually changed companies for their job change, and did not include those who had received a promotion or otherwise changed positions within the same company.
Our research found that 17.6% of data scientists and analytics professionals changed jobs in 2018. For those who changed jobs, their average tenure at their previous position was 2.6 years.
How Job Change Varies Based on Work Experience
As one might imagine, professionals who are earlier in their careers tend to change jobs more frequently than those with more experience. To illustrate this, we separated the sample by years of quantitative work experience and found that 21.4% of data scientists and analytics professionals with 0-10 years’ experience changed jobs in 2018 and their average tenure was 2.6 years, compared to only 14% of those with 11+ years’ experience, whose tenure was 2.8 years.
While we were a bit surprised to see that the average tenure for quantitative professionals with more experience was still relatively close to the average tenure for professionals early in their careers, this might be because there are more quantitative leadership opportunities than ever before as more firms and industries look to embrace predictive analytics capabilities.
Implications for the 2019 Data Science & Analytics Market
While last year’s salary reports showed that salary changes from 2017 to 2018 were rather muted, this year we’re predicting a more noticeable bump in compensation, and an active market seems to support this prediction.
Implications for Employers
Despite the fact that more professionals are entering the data science and analytics fields, employers looking to hire senior-level leaders will still be targeting a much smaller pool of talent, so be deliberate in your approach to recruiting. Putting extra thought into writing an enticing job description helps you make a strong first impression, so I recently shared my insights on how to write stellar job descriptions for senior-level data science and analytics roles.
Implications for Professionals
If you’re a data scientist or analytics professional, whether you’re thinking about changing jobs or looking to stay where you are, our blogs on how to evaluate quantitative job offers and how to negotiate your salary both have great advice.
If you’re looking to break into these fields or change roles within them, our data science and analytics recruiting team recently published a great guide on what skills and tools to learn as well as how to transition your skills. Best of luck!
We hope this information was helpful! If you’re looking to hire predictive analytics or data science talent, or are looking for new opportunities, be sure to connect with us.
Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.
Want highlights from our 2019 Data Science & Analytics report, including salaries, demographic comparisons, and hiring market analysis? Watch our 15-minute RECAP video below!