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Occupation Spotlight

Data Scientist: A Hot Job That Pays Well


Since December 2013, data science postings have rocketed 256%—more than tripling. Houston, San Francisco offer the best salaries for data scientists.

Back in 2012, the Harvard Business Review called data scientist “the sexiest job of the 21st Century.” Six years later, the job has only grown sexier. More employers than ever are looking to hire these skilled digital data jockeys. And while interest from job seekers is growing, Indeed research shows job postings are growing even faster. There may not be enough skilled applicants, so bargaining power in this explosively-growing data scientist job market likely remains with job seekers.

Job postings statistics tell the story. Data scientist postings as a share of all postings on Indeed jumped a full 31% in December 2018, compared with the same period the year before. Yet, that was just another solid year in the spectacular and steady rise in data science jobs on Indeed. Since December 2013, postings have rocketed 256%—more than tripling.

Why is this job growing like gangbusters? It’s because employers use data scientists to solve all sorts of problems. In essence, data scientists are tasked to take raw data and use programming, visualization, and statistical modeling to extract insights, according to the Bureau of Labor Statistics (BLS).



While postings have surged, job searches for data science positions have grown more slowly. Data science searches as a share of all searches rose almost 14% in 2018—a healthy gain, but far less than the rise in postings.


Data science job searches follow something of a seasonal pattern. In 2017 and 2018, they peaked in February and March. This might reflect an influx of students searching for internships and/or soon-to-be graduates looking for their first job. Data science jobs have been hyped for at least six years now and college students majoring in computer science are on the rise.

Given this, what do we know about data scientists? They typically are fluent in one or more programming languages used for statistical analysis, according to the recruiting agency Burtch Works. Languages such as Python and R are data scientist favorites, 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 others. Many data scientists got their formal education in such disciplines as computer science, statistics, or one of the quantitative social sciences. Nearly all data scientists have some training in statistical modeling and machine learning, as well as programming. In essence, the data scientist job mixes rigorous theory and the software craft.

Houston, San Francisco offer best salaries for data scientists

The typical data scientist earns a high salary, with Houston and San Francisco coming in as the top cities in pay. In affordable Houston, the average data scientist salary was about $138,000 in 2018, which translates into about $123,000 after adjusting for the cost of living. The nominal salary in the ultra-expensive San Francisco area averaged a hefty $167,000. When considering living costs, however, that equals a little over $121,000, giving Houston the top spot for data scientist salaries.


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

Data scientist salaries were grouped by Metropolitan Statistical Area (MSA) according to the location of the posting and averaged. Only metro areas with at least 50 qualifying data scientist job postings in 2018 are included. The local cost-of-living adjustment was calculated using US 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. The local cost-of-living adjustment was calculated using US Bureau of Economic Analysis regional price parities for 2016, released in May 2018, using the implicit regional price deflator from Table 4.