This article having six factors that will positively influence the salary of a Data Scientist. So here are six factors through which you can easily optimize your Data Scientist salary.
Data Scientist is needed by every company. Data is generated by the terabytes everywhere, and all organizations, big and small, could benefit significantly from having a Data Scientist on staff. Indeed, Data Scientists are being recruited with ever-increasing frequency in every industry, in both the private and public sectors. While every industry has its pros and cons, the pay for Data Scientists is substantially higher in the following industries, per the most recent O'Reilly Data Science Salary Survey:
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The companies whose websites you spend the most time on are also the ones who have the most data and the biggest need for Data Scientists. If you can get past their rigorous interview process, companies like Google and Facebook will pay a premium for top Data Scientists. According to Glassdoor, the average Data Scientist salary at Facebook is ~$133,000 based on 47 reported salaries, while at Google, the average is ~$138,000, over 5 reported salaries.
Judging by the way things have been going on the stock market these past few years, you'd think that the financial industry's glory days as the first choice for top talent are behind us. However, banks and other financial companies are still competing for top analytical talent, and offer competitive salaries to attract and keep the best.
While manufacturing in the US has been getting more and more automated in the past few decades, it has also been getting a lot more data-savvy. From car manufacturers to the makers of clothing, electronics and convenience foods, manufacturing companies are vying to become employers of choice for data science and analytics professionals. For instance, according to Glassdoor, a Data Scientist at Lockheed Martin makes on average ~$102,000 a year, while the average Senior Data Scientist working for the defense contractor can clear ~$112,000 per year.
We live in an age where software makes the world go â??round, so it is little wonder that software companies big and small are looking to gain a competitive edge by mining their data for profitable insights. In a highly competitive industry such as software, a company is only as good as the people it hires, so compensation tends to be competitive as well, particularly for valuable data science candidates.
Well-funded software startups, in particular, are known to pay their Data Scientists handsomely, and they are also widely seen as good indicators of longer-term trends in data science salaries. However, less-funded or bootstrapped software startups can also be a good bet for Data Scientists looking to maximize their compensation. While the base salary at these shops might not be quite so high, a well-designed equity package can more than makeup for the difference when the company makes an exit.
For those of us who graduated around the turn of the last decade, it might have seemed like there were really only two respectable options for starting a high-paying professional career after college: going into finance (specifically, investment banking), or working as a business consultant at a (preferably large and well-known) consulting company. Nowadays, when coding is sexy, and data science is white-hot, new graduates have a bit more variety in their options.
What is more, in many respects, Data Scientists have replaced management consultants thanks to their promise of extracting tangible value and actionable business insights from the large amounts of data available at every turn.
2. Company size
When it comes to size, companies with 10,000+ employees tend to offer their Data Scientists the highest salaries according to O'Reilly's report. However, beware of companies that have been around for more than 10 years! O'Reilly's Data Scientist salary survey seems to show that these older companies tend to pay their analytics group as much as 15% less on average, compared to their younger counterparts.
Just like there is usually no smoke without a fire, the saying that â??Data Scientists are data analysts who live in California' has some truth to it. While there are Data Scientists in other parts of the country (and the world), their salaries are typically lower than they would be for the same role in California.
According to Glassdoor, the average Data Scientist salary in Seattle is 5% higher than the national average, while the cost of living in Seattle is significantly lower compared to San Francisco and other cities in California.
As might be expected, a Data Scientist's salary will go up as she climbs through the ranks into middle management and beyond. Data Science Managers make as much as $30,000 more on average than an individual contributor, while Directors' and VPs' average compensation could be as much as 120% more than a rank and file Data Scientist's, according to O'Reilly.
Less predictably though, the different titles of individual contributors doing data science work also correlate with a (negative) variation in pay. Data science work comes in many shapes and not all professionals who do it have a Data Scientist title. Research shows that, while Data Analysts or Data Engineers will often do very similar or even the same work as Data Scientists, their salaries are sometimes lower. According to DataJobs.com, a Data Scientist can make as much as 20% more than a Data Engineer, and a whopping 40% more than a Data Analyst.
5. Tools used
When it comes to the tools of the trade, quantity seems to be better than quality. Data Scientists who are familiar with 15 or more tools and technologies have a decided edge over their more specialized peers in terms of salary.
As for those specializing in individual technologies, a positive uptick in salary exists for Data Scientists who use Spark and Scala and, to a lesser extent, for those who are familiar with various tools from the Amazon Web Services suite, or similar cloud computing platforms. In terms of visualization technologies, proficiency with d3.js corresponds to higher salaries for data science and analytics professionals.
If there is one thing that will make a huge amount of difference to your lifetime earning potential, it is your skill as a negotiator. Unlike Python programming, modeling and stats, negotiation skills are not required for a career in Data Science, but you might find them worthwhile to have regardless, if you want to optimize your salary as a Data Scientist.
Fine-tune these six factors during your data science job search and maximize your Data Scientist salary. Want more insider tips on data science, as well as fresh data jobs from top companies