Data scientist salary guide
Data scientists are one of the keystone posts of the entire data science and analytics (DSA) complex. For this reason data scientist salaries are growing nicely. The role of a data scientist is to bring sense to the reams of data that pour into today’s corporate management offices. By combining a knowledge of statistics, business logic, and computer programming, data scientists design, create and implement programs that pull the data into reports that managers can use to make decisions that improve a company’s trajectory. A full guide to data scientist trends, career development, required skills and qualifications, and future outlook can be viewed here. Because of advancing technologies and the recognition of the value of strong data analytics, the need for DSA skills and knowledge has never been higher, nor growing faster. And the resultant demand for data scientists has left the workforce with an expanding shortage of job candidates as compared to the current and expected demand. There can thus be no surprise that data scientist salaries are strong and continuing to escalate.
Data scientist salary overview
The average salary for data scientists is approximately $96,000 per year, according to PayScale.com. Bonuses, commissions and profit sharing can add about another $20,000 on average. SalaryList.com shows data analyst average salary at $107,000. Glassdoor.com puts the average data analyst salary at $117,000.
Data scientist salary trends
Data scientists are in high demand. According to a research study done by Burning Glass Technologies, there were 2.35 million job listings for all positions categorized as data science and analytics (DSA) in 2015. According to the company’s research, that number will have increased to 2.72 million jobs by 2020, representing a 15% per annum growth rate over 5 years. The fastest growing specialties within the broader DSA category are data scientists and advanced analysts. The pressure that this staff demand puts on salaries is real, and employers are having to adapt their pay scales quickly in order to attract new employees and keep existing staff.
Robert Half Technologies’ (RHT) 2019 Salary Guide provides average beginning salaries for new data science hires across different percentiles to indicate experience level of those new hires. According to RHT, the least experienced candidates were being given an average annual salary in the $102,000 range, while applicants with the most experience were being offered about $175,000 on average. So despite the fact that demand for data scientists is growing more rapidly than the available supply, employers are focusing on more experienced new hires, and those experienced data scientists are making it pay.
Burning Glass’ 2015 Quant Crunch research report states that 78% of data scientist job openings requested candidates with at least three years of experience, and 39% requested applicants with a master’s degree or higher. Entry-level candidates still represent 22% of new job postings.
Data scientist salaries as experience increases
Data scientist salaries are highly dependent upon the experience level of the job candidates, and the increasing salaries persist across all ranges of the experience spectrum, even accelerating for data scientists with more than four years under their belts. PayScale.com reports that average salaries for candidates with five to nine years experience garner salaries on average about 9% higher than entry-level employees. After reaching the 10-year mark, salaries for data scientist salaries tend to grow at a 12%-13% annual rate.
The two skills that tend to give data scientist salaries the biggest boost are:
- Machine learning
- Python
In addition, taking the time to obtain a masters degree in data science can be an extremely lucrative achievement.
Industries most in need of data scientists
According to Burning Glass, there are three economic sectors employing 59% of all DSA employees. These industries are finance and insurance, professional services, manufacturing, and information technology. Professional services companies hire 34% of the data scientist workforce, and finance and insurance companies hire 23%, while manufacturing companies supply 10% and IT companies 9%. The three highest paying industries for data analysts are professional services ($97,000 average), finance and insurance (average $106,000), and manufacturing (average $92,000).
Geographic variations in data scientist salaries
General cost of living and economic conditions vary across the United States, from state to state and city to city. These variations have a consistent impact on pay scales in nearly all career categories, with few exceptions. These exceptions tend to be in newer job types, or in industries that are experiencing major disruptions of some sort, whether temporary or more cyclical in nature. While the data science industry in general, and data analysts in particular, are increasingly experiencing labor shortages, salaries for data analysts at present tend to fluctuate geographically in near lockstep to average salaries across all job descriptions. National cost of living changes by geography can be viewed at moneygeek.com.
Highest paying companies for data scientists
SalaryList.com lists some of the higher paying employers of data analysts to include:
- Microsoft $129,000
- Facebook $145,000
- Amazon $118,000
- Apple Computer $137,000
- Google $140,000
- IBM $113,000
- Capitol One $113,000
- Uber $125,000
- LinkedIn $129,000
- Zillow $122,000
As can be seen above, the largest companies with the greatest need for data and data analytics are paying the highest data scientist salaries, which doesn’t come as a surprise.
Data scientist career paths and salary impact
Nearly every industry can benefit from the knowledge that can be gained by good data science. The ways in which companies collect data, and the data points being targeted are still growing rapidly. This means data scientists are and will continue to experience high demand. By learning Python and machine learning, data scientists can significantly increase their value.
By simply staying in the data science role throughout a career, professionals can experience strong salary growth. But by making the jump to data engineer, earning power can increase even more dramatically, potentially as much as 25%, according to Robert Half Technology.
Climbing straight up the corporate ladder, to data analytics management positions or database manager will also provide a bump in income, while also leaving open the possibility of moving even higher up the influence scale.