Burtch Works recently released our annual comprehensive salary study focusing on two increasingly popular areas of the quantitative market: Predictive Analytics and Data Science. While this is our sixth annual study, this is the first time we have combined the two fields into a single report, with the intent of comparing the two groups.
The two career paths have become more and more similar in recent years, but there remain some notable differences between Predictive Analytics Professionals (PAPs) and Data Scientists that make such a direct comparison valuable.
PAPs possess well-refined quantitative skills that can be used to describe or derive insights from large datasets. Burtch Works classifies Data Scientists as a subset of PAPs; in addition to the skills that predictive analytics professionals possess, they also have the computer science skills necessary to work with unstructured or streaming data, including formats like audio or video files.
The study focuses on compensation and various demographic information, including education, residency status, industry, gender, and experience levels. The report also includes general trends for the overall data science and analytics hiring market.
2019 Data Science & Analytics Hiring Market Trends
The most noticeable market trend was that professionals in these fields have more options than ever as demand for their talents has grown. An increasing number of companies and industries employ PAPs and Data Scientists, which has also made these fields more technically diverse.
Education is another area where there are more options than ever for those looking to enter these fields. New education programs, such as coding bootcamps and online programs, have allowed professionals to transition their skills to data science and analytics roles from related fields. Although the majority of data scientists and analytics professionals have a graduate degree, the study found that an increasing number of professionals with eight or less years’ experience held a Bachelor’s as their highest degree.
As more professionals are supplementing their skillsets through online programs, more and more companies are relying on technical screens testing to better evaluate talent coming from a variety of educational backgrounds. These technical screens allow companies to evaluate a candidate’s technique and tool fluency, as well as problem-solving capabilities.
You can read more about 2019 hiring market trends here.
2019 Salaries of Predictive Analytics Professionals and Data Scientists
Our study was conducted with a sample of 1840 PAPs and 421 Data Scientists. These two groups were then divided between individual contributors and managers, which were then subdivided into 3 job levels indicating their level of work experience or management responsibility. Changes in salaries were compared using the median of the years 2019 versus 2018 instead of focusing on the salary changes of individuals.
For individual contributors in Predictive Analytics, median base salaries increased at all job levels; the largest increase was observed at the earliest career level (level 1). Results were a little different for PAPs that were managers. At levels 1 and 2, median salaries showed no change, while the most senior managers (level 3) saw a 4% increase. Across all levels of all PAPs, the mean and 75th percentile salaries increased.
The story was similar for Data Scientists, who also saw modest increases or no change to their salaries when compared to the previous year. Individual contributors at level 1 saw no change in their median compensation, while at levels 2 and 3, salaries saw a 1% increase from 2018.
Compensation at levels 1 and 2 increased slightly for Data Scientist managers, who saw an increase in their median salaries of 1% and 3%, respectively. Level 3 managers saw no change in their salaries, with a median compensation of $250,000.
With this data in mind, we then compared the salaries of professionals in predictive analytics and Data Scientists. Among individual contributors, Data Scientists out-earned their PAP counterparts at all levels, with the largest difference being at level 2 individual contributors, with a 34% discrepancy, and the smallest being at level 1, where there was a difference of 19% in compensation.
Managers in Data Science also earned more than managers in Predictive Analytics, but the difference was less pronounced than it was for individual contributors. The largest difference was between managers at level 1, where Data Scientists earned 12% more than PAPs at this level; at level 3, this shrank to a difference of 1%.
You can find salary quartiles, medians, and means for both groups starting on page 15 of the full report.
Education Profile of Predictive Analytics Professionals and Data Scientists
Our study also used various demographic data collected to focus on other trends for both PAPs and Data Scientists at various areas of interest, including education (highest degree held).
We found that 86% of PAPs in the sample held an advanced degree, with 71% having a Master’s degree and another 15% of the sample holding a PhD. 94% of Data Scientists in the sample had an advanced degree; 47% held a Master’s degree and another 47% had earned a PhD.
As one would expect, those in Predictive Analytics who held an advanced degree tended to earn more than those whose highest level of education was a Bachelor’s degree, but this was not true at all job levels. However, PAPs with a PhD out-earned holders of a Master’s degree at all levels.
Data Scientists with a PhD earn more than those with a Master’s degree at every level except level 2 and 3 managers. In some managerial positions at higher levels, leadership and management skills often matter more to employers than education.
Mathematics and statistics were the most common field of study in terms of highest degree for both PAPs and Data Scientists, but there were some key differences. PAPs were significantly more likely to have a degree in economics or business, while Data Scientists were more likely to have a degree in computer science or engineering. The business degree category includes Master’s in Business Analytics degrees, which have become increasingly popular in recent years.
You can see how salaries vary depending on degree starting on page 22 of the full report.
Gender of Predictive Analytics Professionals and Data Scientists
We also studied gender distribution between PAPs and Data Scientists in the report. 26% of the sample for Predictive Analytics Professionals were women; for Data Scientists, 17% of the sample were women. Compared to last year, the sample showed an increase of 2 percentage points for women in predictive analytics and an increase of 2 percentage points for women Data Scientists.
The largest percentage of women for both Data Scientists and PAPs was among level 1 individual contributors, indicating that more women are entering the field. This may indicate that the gender ratio will become more equal as these junior workers become more experienced.
Women who were PAPs earned slightly less than men at most levels for both individual contributors and managers except level 1 managers. The pay gap we observed was relatively small, however; the largest gender salary difference in the sample was among level 3 individual contributors, where women earned 94% of what men did.
You can also see how analytics salaries vary by gender on page 38 of the full report.
The most common industry for PAPs was financial services, where 24% of the sample worked. 18% worked in advertising/marketing, the second most common industry, and 12% were employed in the tech/telecom/gaming industry.
Data Scientists were overwhelmingly employed in the tech/telecom/gaming industry; 48% of the sample worked in this field. This was followed by businesses involved in financial services, with 12%.
You can see analytics salaries examined by industry starting on page 34 of the full report.
A sizeable portion of both Predictive Analytics Professionals and Data Scientists working in the US are not citizens. The study found that 27% of PAPs have some form of visa or permanent residency status, while that percentage is 33% for Data Scientists sampled.
For those in predictive analytics, 13% of the sample were permanent residents, 10% had an H-1B visa, 3% had an F-1/OBT, and an additional 1% had some other form of visa. For Data Scientists, these percentages were 19%, 9%, 4%, and 1%, respectively.
Predictive Analytics Professionals became increasingly likely to be residents as job levels increased at all levels for both individual contributors and managers. The group with the highest percentage of citizens was at level 3 managers, where only 13% of the sample were not citizens. This trend was the same with most levels of data scientists; the exception was at level 2 individual contributors, where there were less citizens than at level 1.
You can see how residency status varies depending on job level, and how salaries vary depending on residency status starting on page 26 of the full report.
In addition to the data we highlighted above, the 50-page report also has plenty of data and insights that would be too lengthy to include here, including:
- How we define data scientists vs. predictive analytics professionals
- Why we segment these two groups for examination
- Salaries examined by geographic region, industry, education, gender, and more
- How years of experience varies for both fields
- Our data collection and study design