This post is an adapted excerpt from our recently-released 2020 Burtch Works Study, which examines salaries and demographic data for data scientists and predictive analytics professionals. Download the full report for free here.
2020 has been a year of massive disruption all across the US workforce. A global pandemic, a mass social justice movement, and a presidential election year, among other forces, are all having an incredible impact on our country, our lives, and the way we work.
Although we released this report in August, our data collection period ended in April, which meant it was unlikely that these impacts would be apparent in our study data quite yet. However, for the past few months, in addition to our data analysis for this annual report, we’ve been hard at work gathering data that is more immediate to the current work environment as it continues to shift.
Since these two collections of data are separate, in this post we wanted to cover some of the trends we observed in this year’s Burtch Works Study data, and in our next post we’ll be sharing additional data and insights we’ve been gathering specifically on COVID-19’s impact to the data science & analytics hiring market, even though they are not technically reflected in the rest of this report.
2020 Salary & Demographic Trends
Our salary studies report base salary variations of PAPs and data scientists, both individual contributors and managers. We also report how base salaries have changed since last year’s study. Finally, the report explains how salaries of PAPs and data scientists vary based on several characteristics including job level, industry, region, education, residency status, and gender.
Burtch Works differentiates data scientists from other predictive analytics professionals (see Identifying Data Scientists & Predictive Analytics Professionals on page 48), and reports their respective salaries in this study. Historically, data science salaries are quite different than those seen in predictive analytics, and even though there are some indications of the two groups blending, the compensation trend continues this year
For detailed analysis on this year’s compensation trends, demographic trends, and other insights, you can download the full report for free here.
1. Salaries Remained Mostly Steady, With Some Modest Increases
Across all job levels for both data scientists and predictive analytics professionals (PAPs), salary medians remained within a few percentage points of what we reported in 2019. While the median for data scientists at individual contributor level 3 decreased by 1%, all other levels either showed no change or increased from 1-3%. For additional details about how salaries vary from last year, see Section 2. For complete information about how salaries vary by demographic characteristics, see Section 3.
2. Data Scientists Continue to Earn Higher Salaries at Most Levels
In keeping with previous years, data scientists continue to earn higher salaries than other predictive analytics professionals at comparable job levels, and the differences are the most evident for individual contributors where data scientists earn from 19-30% more than PAPs.
This can be due to several factors including a higher prevalence of PhDs among data scientists, highly specialized skillsets required to analyze unstructured and/or streaming data, and smaller talent pool that can all drive up salaries. This salary premium decreases for managers and median salaries are equal for both data scientists and PAPs at MG-3, which is likely due to the fact that in leadership positions, management skills tend to be more critical than educational background and heavy technical expertise.
Burtch Works considers a data scientist to be a specific type of predictive analytics professional. Both groups analyze data to glean insights and prescribe action, but data scientists focus on cleaning and analyzing unstructured or streaming data, using sophisticated computer science and programming skills that are not typically seen in the profiles of other predictive analytics professionals
3. Early Career Professionals Continue to Flock to Data Science & Analytics
This year, the median years of experience for both samples was 6.0 years. A large percentage of professionals in both predictive analytics and data science have 10 or fewer years of experience: 64% for PAPs and 72% for data scientists (see Figure 30 on page 45). The increased visibility of both careers in recent years has led to a surge of interest from both students entering the market and career changers alike, which has caused both samples to skew further towards the early career levels.
4. Percentage of Women at Early Career Level is Rising
When examining the gender composition of different job levels in our predictive analytics sample over time, we’re able to see a noticeable increase in the percentage of women individual contributors at level 1 (IC-1). In 2015, our IC-1 sample (professionals with 0-3 years’ experience) was 28%, which has risen to 36% in 2020.
5. Business Degrees Losing Favor Amongst Both Groups
Looking at the degree specialty for the highest degree earned in both samples, there was a noticeable decline in the prevalence of Business degrees (which includes MBAs and Business Analytics degrees) for both groups. For PAPs, we noticed an increase in Math/Stats degrees, while in data science there was an increase in Engineering degrees. This may be due to candidates opting to strengthen their quantitative credentials for greater career opportunities over the more general MBA track.
While this data reflects the state of the quantitative hiring market primarily before the COVID-19 pandemic, we also wanted to share some of our predictions and insights on trends that have emerged since we collected this data, as well as impacts we may see reflected in next year’s data.
Learn more about these trends and others by downloading our full report or view the webinar recording below!