Chapter 5. Conclusion
THE DATA SCIENTIST ROLE CONTINUES TO GROW globally as more nonsoftware companies understand the need for resources to analyze and report on data using modern tools. Overall, results are similar to what we found in 2016, helping confirm the reliability of the survey data. Most of the salary data shows stable trends, with a few sectors showing increases—what we expect as the years pass.
There were a few surprises, such as the shuffle of programming languages and relative drop in US representation. This change might be due to the types of companies responding to the survey or other trends in the market. Some software releases garner intense attention, creating a rush to try and learn a new programming language in order to make use of new features or libraries.
Low usage rates for tools (e.g., languages and databases) doesn’t imply some inherent deficiency. These tools might address niche functionality, legacy systems, or a functional area on the cusp of more widespread adoption.
This report is our best guide to what is happening in the industry surrounding data. Use the report to start conversations with your team and company regarding tools and processes and help map out the elements that make up the data landscape and how that relates to your company’s technology infrastructure and business model. Look at what soft and hard skills you should consider in order to stay competitive and relevant in the data ecosystem. With the guidance of producing five years of reports, we look forward to continuing to conduct and share the salary surveys with the data community.
Model
The model has an R-squared of 0.60: this means the model explains approximately 60% of the variation in the sample salaries. Geography is used as the Y-axis intercept of the model. Select the appropriate location and then proceed through the coefficients, adding or subtracting the ones associated with a feature that applies to you. After you sum up the coefficients, you will obtain the model’s estimate for your annual total salary in US dollars.
World Region
Canada: $43,991
Western Europe: $38,113
Asia: $23,542
Eastern Europe: $6,180
Latin America: $5,559
Australia/New Zealand: $63,067
Africa: $17,145
US Region
Midwest: $68,087
California: $101,834
Texas: $73,048
Mid/Atlantic: $84,487
Southwest/Mountain: $73,327
Pacific/Northwest: $79,525
Northeast: $87,944
Experience
+$2,464 (per year)
Gender
Male: +$8,071
Company Size
1: –$19,402
2–25: –$21,442
26–100: –$13,581
101–500: –$9,753
501–1,000: –$8,484
1,001–2,500: +$13,951
2,501–10,000: –$5,708
Education
PhD: +$5,376
Job Title
Data scientist/analyst: +$14,227
CxO: +$42,209
VP/Director: +$45,678
Product/Project manager: +$10,345
Architect/Technical lead: +$24,001
Marketing: +$11,092
System engineer: -$13,872
Industry
Healthcare/Medical: –$8,505
Consulting: +$4,474
Retail/Ecommerce: +$12,594
Government: –$10,050
Nonprofit/Trade association: –$21,545
Logistics: +$18,171
Search/Social networking: –$11,885