Earlier this month, Salesforce announced the acquisition of data visualization and analytics leader Tableau for $15.7 billion and Google announced the acquisition of data discovery and analytics platform Looker for $2.6 billion. Both acquired companies will beef up the acquiring companies’ Data Science as a Service (DSaaS) capabilities, providing their enterprise customers with a wide range of easy (or easier) to use tools that “democratize” data preparation, integration, analysis, and presentation.
With self-service data science, all business users that do not have statistical analysis background and don’t know how to code can make data-driven decisions, instead of relying on expensive and hard-to-find data scientists.
How expensive? The average annual base salary for an experienced data scientist in a management position is currently $257,443 according to Burtch Works. The executive recruiting company released today its 6th annual survey of data scientists’ compensation, this year combining it with its annual study of predictive analytics professionals. Data scientists, says the report, “work primarily with unstructured or streaming data and therefore command higher salaries than others in predictive analytics that mostly focus on structured data.” Here are the key findings for data scientists:
- Median base salaries for individual contributors range from $95,000 at Level 1 to $167,000 at level 3.
- For managers, median base salaries range from $146,000 at level 1 to $250,000 at level 3.
- In comparison to 2018 data, median base salaries for 2019 have either remained steady or risen slightly at all levels. Salary means increased at all levels, and salaries at the top quartile increased significantly in some cases. This may indicate that salary ranges are stretching and could be a sign of future salary growth.
- While the median base salaries show that data scientists earn more than predictive analytics professionals at all levels, the differences are the most evident for individual contributors where data scientists earn from 19-34% more.
- Data science is a young discipline: 66% of data science professionals sampled have fewer than 10 years of experience.
The trend toward specialization Burtch Works highlighted last year continues, helped by the increasing adoption of AI: “Companies looking to drill down on specific use cases are focusing more on data scientists that specialize in particular areas, such as NLP (natural language processing), Computer Vision (image processing), or even specific domain experience like AdTech or IoT (internet of things). Of course, as demand becomes more specialized, this also narrows the potential talent pool to draw from.”
The overall talent pool is growing with the constant fresh supply from the numerous graduate programs in data science and business analytics. Still, demand is also growing rapidly, driving US enterprises to recruit data scientists from outside the US. 33% of data scientists surveyed by Burtch Works are non-U.S. citizens with permanent residency or an F-1/OPT, H-1B, or another visa which allows them to work in the U.S. This is up from 13% of data scientists holding an H-1B visa in last year’s sample and way up from 9% in 2015 and 2016 and 8% in 2017.
This year’s sample consists of 421 data scientists and 1,840 predictive analytics professionals. Burtch Works collected the data for this study during interviews conducted over the months immediately following the period of interviews for the 2018 studies, with data collection ending in April 2019.
Data science continues to be one of the most lucrative professions today and demand for data science skills and experience will continue to grow. There’s also no doubt that we will see this year further consolidation in the industry with additional mergers (e.g., Sisense combining with Periscope Data) and acquisitions (e.g., Intuit acquiring Origami Logic).
Burtch Works will discuss its findings in a webinar on June 20, 2019.