Originally posted here.
Last year, there was much made of an unconfirmed report that a developer at Google was making $3 million a year. It made many people speculate that the high demand for data scientists coupled with the low supply was creating a salary bubble.
IT World couldn’t confirm the $3 million developer story, but it did some number crunching from publically available data and determined that the average developer at Google makes around $145,000 per year, including stock and bonuses, while the highest paid senior engineer might make as much as $1 million including stocks and bonuses.
But that’s Google. What can a data scientist expect to make elsewhere?
According to Glassdoor, the current U.S. average salary for a data scientist is $118,709, but it varies widely based on a number of factors.
One major factor that will determine salary levels is your actual title and relevant job skills and responsibilities.
- Database Administrator (DBA): $50,000–$120,000
DBAs are responsible for the upkeep of the databases that store all that data. Salaries depend on the person’s level of experience and the complexity of the system being maintained.
- Data Analyst: $50,000–$110,000
A data analyst is a quant-focused professional and generally has a BS or MS degree. Entry-level analysts can expect to make $50k and up, while senior level analysts can reach six figures.
- Data Scientist: $85,000–$170,000
A data scientist is an experienced, expert-level professional (there’s no such thing as an entry-level data scientist) and are paid accordingly. Some of the variants in pay come from the topics and applications in which the person is well versed.
- Data Scientist/Analyst Manager: $90,000–$240,000
When a person rises to the level of manager, pay is often based on the number of people directly reporting to that manager. Low end would be 1–3 subordinates, while the high end would be 10 or more employees.
- Data Engineer: $70,000–$165,000
The data engineer is the person designing the systems that record and store the data. They must have a wealth of programming knowledge as well as expertise in informational architecture.
Another factor is the type of company you work for. According to an O’Reilly survey from 2013:
- Startups pay the most, with a median salary of $130k.
- Public companies paid a median salary of $110k.
- Government and education sector jobs were the lowest at $80k.
Glassdoor also has interesting data from anonymously reported salaries that shows averages at different companies. For example, data scientists at Groupon right now are reportedly earning the most at an average of $164k while Booz Allen Hamilton is paying closer to $94k for the same job title.
Likewise, the type and number of tools a person uses on the job can increase the salary range. Salaries show data scientists who use the Hadoop cluster got paid more; average salary for those who don’t use it was $85k compared to $125k for those who use at least six tools or more.
Salaries depend on so many factors that it’s impossible to point to a single number and say, “This is what you should be earning.” But it can be helpful if you are a data scientist (or are considering the field) to understand where your own salary fits into national averages.
Are you a data scientist or do you employ one? How does this information tally with your experience in the market? I’d love to hear your real-world experiences in the comments below.
As always, I am always keen to hear your views on the topic and invite you to comment with any thoughts you might have.
About : Bernard Marr is a globally recognized expert in analytics and big data. He helps companies manage, measure, analyze and improve performance using data.
His new book is: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance You can read a free sample chapter here
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