O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30,000 in Eastern Europe, and highlights that negotiation skills can lead to salary differences as high as $45,000. Other key factors impacting earnings include company age and size, job title, industry, and education, while popular tools and languages—such as Python, SQL, and Spark—do not strongly influence salary despite widespread use.
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Global and Regional Salary Differences
- Median Global Salary: $90,000 USD, up from $85,000 the previous year.
- Regional Breakdown:
- United States: $112,000 median; California leads at $134,000.
- Western Europe: $57,000—about half the US median.
- Australia & New Zealand: Second after the US.
- Eastern Europe: Below $30,000.
- Asia: Wide interquartile salary range, indicating high variability.
Demographic and Personal Factors
- Gender: Women's median salaries are $8,000 lower than men's. Women make up 20% of respondents but are increasing in number.
- Age & Experience: Higher age/experience correlates with higher salaries, but the proportion of older professionals declines.
- Education: Nearly all respondents have at least a master's; PhD holders earn only about $5,000 more than those with a master's.
- Negotiation Skills: Self-reported strong salary negotiation skills are linked to $45,000 higher median salaries (from $70,000 for lowest to $115,000 for highest bargaining skill).
Industry, Company, and Role
- Industry Impact:
- Highest salaries found in search/social networking and media/entertainment.
- Education and non-profit offer the lowest pay.
- Company Age & Size:
- Companies aged 2–5 years offer higher than average pay; less than 2 years old offer much lower salaries (~$40,000).
- Large organizations generally pay more.
- Job Title:
- "Data scientist" and "data analyst" titles carry higher medians than "engineer" titles by around $7,000.
- Executive titles (CTO, VP, Director) see the highest pay, with CTOs at $150,000 median.
Tools, Languages, and Technologies
- Operating Systems:
- Windows: 67% usage, but declining.
- Linux: 55%; Unix: 18%; macOS: 46%; Unix-based systems are rising in use.
- Programming Languages:
- SQL: 64% (most used for database querying).
- Python: 63% (most popular procedural language).
- R: 54%.
- Others (Java, Scala, C/C++, C#): Each less than 20%.
- Salary difference across languages is minor; C/C++ users earn more but not enough to outweigh the difficulty.
- Databases:
- MySQL (37%), MS SQL Server (30%), PostgreSQL (28%).
- Popularity of the database has little impact on pay.
- Big Data and Search Tools:
- Spark: Most popular big data platform, especially for large-scale data processing.
- Elasticsearch: Most common search engine, but Solr pays more.
- Machine Learning Libraries:
- Scikit-learn (37%) and Spark MLlib (16%) are most used.
- Visualization Tools:
- R's ggplot2 and Python's matplotlib are leading choices.
Key Salary Differentiators (per Machine Learning Analysis)
- Top Predictors (explaining ~60% of salary variance):
- World/US region
- Experience
- Gender
- Company size
- Education (but amounting to only ~$5,000 difference)
- Job title
- Industry
- Lesser Impact: Specific tools, languages, and databases do not meaningfully affect salary.
Summary Takeaways
- The greatest leverage for a higher salary comes from geography and individual negotiation capability, with up to $45,000 differences possible.
- Role/title selection, industry, company age, and size are also significant, while mastering the most commonly used tools is essential but does not strongly differentiate pay.
- For aspiring data professionals: focus on developing negotiation skills and, where possible, optimize for location and title to maximize earning potential.
