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Data Scientist Is Still a Hot Job and Pays Well Too

Data Scientist: The Sexiest Job of the 21st Century.” So proclaimed the Harvard Business Review in 2012. Six years later and the job of a data scientist has only grown sexier. More employers than ever are looking to hire data scientists. Yet while the supply of data science job seekers is growing, it’s far outstripped by the rising demand in postings, meaning there potentially may not be enough skilled applicants. So the bargaining power in data science remains with the job seekers, according to new Indeed research.

Job postings for data scientists as a share of all postings were up 29% in December 2018 compared to a year before. This is just another solid year in what has been a spectacular and steady rise in the number of data science jobs on, the world’s largest job site. Since December 2013, postings are up 344% — more than quadrupling over five years. Employers use data scientists to solve all sorts of problems. In essence, data scientists are tasked to “transform raw data into meaningful information using data-oriented programming languages and visualization software,” according to the Bureau of Labor Statistics (BLS).

While postings have surged, job searches for data science positions have grown more slowly. Searches were up almost 14% in 2018.

Data science job searches follow a somewhat seasonal pattern. In 2017 and 2018, searches peaked in April or March. This might reflect the influx of students searching for internships and/or soon-to-be graduates looking for their first job. After all, the job data scientist has been hyped for at least six years now and college students majoring in computer science are on the rise. But as the growth in postings for data scientists shows, even with that boom, there potentially may not be enough skilled applicants, as demand might be outpacing supply.

Who are these job seekers and which ones are likely to become data scientists? According to Burtch Works, a recruiting agency, data scientists typically have some fluency in one or more programming languages used for doing statistical analysis and deploying prediction models in a software ecosystem. Languages such as Python and R are favorites of data scientists, according to Kaggle, a platform for data science competitions. But they also use a slew of other technical tools like Hive, BigQuery, AWS, Spark, and Hadoop, among several others. Many data scientists’ formal education is in a discipline like computer science, statistics, or a quantitative social science. Nearly all data scientists have some training in statistical modeling and machine learning, as well as programming. A mixture of rigorous theory and software craft.

Houston, San Francisco offer best salaries for data scientists

The typical data scientist earns a high salary. The top cities for data scientists are Houston and San Francisco, with average cost-of-living-adjusted salaries of just over $120,000. In the ultra-expensive San Francisco area, in real terms this translates to an average data scientist salary of $167,000. San Francisco pays well both in absolute and relative terms for data scientists. Whereas in cheaper Houston, the actual average salary is about $138,000.

Boston had the greatest number of qualifying postings in 2018, with the New York metro area second and San Francisco third. For comparison, the average wage nationwide for Computer Programmers was $88,000 in 2017, according to the BLS. Yet it was $107,00 for Software Developers, Applications.

The table below lists the metro areas with at least 50 qualifying job postings in 2018 for a data scientist:

Job postings and searches on were taken from December 1, 2016 to January 1, 2019, and calculated as a share of all postings and searches. Postings were identified by having “data” and either “science” or “scientist” in the job title. Searches were identified by having any version of “data science” or “data scientist” in the search query.

Data scientist salaries are grouped by Metropolitan Statistical Area (MSA) according to the location of the posting and averaged. The local cost-of-living adjustment was done using data from the U.S. Bureau of Economic Analysis regional price parities for 2016, released in May 2018. This cost-of-living data reflects local differences in the price of housing, other services and physical goods.

About the Author

Andrew Flowers is an Economist at Indeed.


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