How Much Do Data Scientists Make?
Wondering how much data scientists make? We're here to help you find out about salaries in Data Science and how they are influenced by various factors.
Recent years have seen a surge in interest in the realm of data science. According to the Bureau of Labor Statistics, there are almost 160,000 data science positions across the United States, The demand in every industry to handle the modern era's abundance of available information means these trends are likely to continue in the long term, despite the recent tech lay-offs. It's only natural to wonder how much data scientists make with a data science role. However, this vitally important topic of how much a person earns as a data scientist is also an incredibly sensitive topic that may be hard to find good information about.
Of course, it is easy to find quick mean and median salaries off of resources such as Glassdoor, Indeed, and Payscale, but that is highly crowdsourced information, and there is naturally some level of skepticism about the data. That's not to mention a moderate degree of variance between the provided data scientist salaries of each site, as well as confounding factors around compensation that can further obscure the actual information, such as career progression, company size, industry, and location, in addition to the other elements of the total compensation outside of just the base data scientist salary.
We're here to help! Let's take a look at salaries in data science and how they are influenced by different factors.
How much can you make as a data scientist?
Let's cover how much you can earn with a data science role and what are the factors that influence your total salary.
Elements of Data Scientist Compensation
First and foremost, remember that the base salary is not the only element of how you are compensated for your job. The "Total Compensation" of a job offer includes all sorts of other benefits that employers use to retain their current employees and attract new ones, benefits that can often be assigned a numerical value in and of themselves. These benefits can include insurance coverage, retirement plans, stock plans, and more.
Health care coverage is a standard benefit, varying with available employer-provided insurance plans and percentage level of employer contribution, up to full coverage. Other insurance coverage outside of health care that may be covered by an employer can also include dental, disability, and life insurance.
Access to a retirement plan, such as a 401k, is another standard benefit for employees to save a portion of their paycheck to invest towards retirement, and can sometimes come with the additional perk of a match from the employer.
Many private companies also provide a stock plan for their employers, tying additional employee compensation to the value of the company. Though the exact structure of that availability can differ company by company, there's always the potential for the appreciation of the company value, and therefore the value of the stock. The possibility of acquisition or of the company going public can create further value for the employee.
Bonuses are another additional element of the total compensation, and can be tied to both individual and company performance.
Additional Miscellaneous Benefits
Additional miscellaneous benefits may be provided as well. Big-name companies, such as those in FAANG, are known to provide a wide variety of free food and snacks for their employees. Companies may also give discounts on their products for their staff, or even provide them to their employees for free. Various stipends for technology, utilities, physical and mental health, and more are also common additional benefits.
Remember that elements of your total compensation balance each other out, meaning that great benefits can mean a reduction in your base salary as a data scientist, and thus the amount of money you take home. For example, startup compensation will often be lower in pay compared to larger companies, but have larger stock options that can potentially balance that out. When considering how you are compensated for your role, it is worth calculating the comparative value of the benefits you receive to the offered base data scientist salary. You could be willing to take a cut to the base salary if that's offset by the additional value of the benefits package.
Aside from your total compensation are all sorts of intangibles within your work environment that are also valuable considerations, such as your personal interest in the field or company, your fit with the company culture, and the potential for growth in the company or in your career. While these may not be immediately obvious when considering initial data science job offers or postings, they are important factors to keep in mind once you start working.
For convenience, our numbers will stick to comparing the base salaries of data science roles, but as you read on, keep that distinction between the base and total compensation in mind.
Data Scientist Salary by Career Progression
You will naturally earn more the further you progress in your career, as you become increasingly able to move up over time by leveraging your experience. Entry level roles can steadily promote into more advanced roles, such as from Data Analyst I to Data Analyst II. Over time, that experience can be leveled up into senior positions or even management roles, such as Senior Data Scientist or Data Scientist Lead. As you gain seniority, your salary numbers will also increase accordingly.
The nature of certain roles also lends to a difference in their compensations. For example, Data Scientist roles generally require a few more years of experience than the equivalent levels of Data Analyst roles, and provide a slightly higher median salary. As such, the required years of experience may translate into someone working as a Data Analyst for a few years before becoming a Data Scientist. This also means that Data Analyst responsibilities can often build up into the required skills for Data Scientist roles.
However, it is important to remember that different companies may have slightly different standards for what an exact title or role may mean. A Senior Data Scientist in one company can have very different responsibilities and compensation compared to a Senior Data Scientist at another company, or maybe even the same as a Data Analyst II at yet another organization. There may also be company-specific internal "levels" for each role. Whereas one company may only have a Data Analyst and Senior Data Analyst, a larger company could have additional intermediate steps of Data Analyst II and Data Analyst III or even more.
In any case, make sure to stay aware about the extent of responsibilities of your role, especially in comparison to broader standards of what a role entails. For example, ensure that you are not a Data Scientist still doing pure data entry work. Understand what responsibilities you have, as well as the potential for growth for your role within your company, and more broader for your career in the long run.
We compare the data scientist salaries of different titles below. Notice the variance from more entry level roles such as Junior Data Analyst to senior management positions such as Data Director. These numbers are in comparison to the statistics from the Bureau of Labor Statistics, which capture the full spectrum of Data Science role levels, and list the national median wage for Data Scientists and Mathematical Science Occupations as $103,500, and the mean salary as $115,240.
The below table shows the salaries from Glassdoor, Indeed, and PayScale. It shows base salaries, except where noted differently.
We also compare the respective skills that are generally required for each role. While more junior data science roles generally have more explicit coding language or software needs, more senior roles such as Senior Data Scientist or Machine Learning Engineer become more conceptual and are broader skill applications. The following table gives a good look at valuable skills you would want to develop the further along you are in your career.
Data from PayScale Skills. Check out Levels.fyi for additional company-specific data.
Now that we've covered data scientist salary by specific role, let's take a look at how it is affected by the company you're working at.
FAANG vs Top Tech vs the Rest
Needless to say, smaller companies will likely have a reduced total compensation compared to the super massive international conglomerates that are FAANG. Of course, smaller and more medium-sized companies will still make an effort to keep their offers competitive. For example, as mentioned previously, startups often offer greater stock options to their employees to compensate for the reduced base salary.
We break down some specific Data Analyst salaries for a few well-known companies below.
We take a deeper dive in comparing the individual companies of FAANG in our Ultimate Guide to the FAANG Companies.
As you look at company data science salaries, you may notice that, company size aside, similar companies have similar compensations. This follows broader patterns of how data science roles are paid by industry.
Data Scientist Salary by industry
The abundance of available data means there is an increasing availability of Data Science roles in every industry. While tech companies like FAANG might be the first names that come to mind, and will in all likelihood be the most lucrative positions, there is also a range of other spaces to work in, depending on opportunity and availability, and your personal background, experience, and interests. Of course, salary will vary according to what sector you are working in.
The following infographic breaks down the data scientist salaries in different sectors and industries.
As expected, the IT sector has the highest salaries. The other industries are around or slightly below the national median. Only education is significantly lower at $125k.
Data Scientist Salary by Location/City
It's a well-known fact that jobs in urban tech hubs pay some of the highest salaries. As such, even within the same company, where you are physically located plays an important role in how much you are compensated. Of course, this is now changing in the post-pandemic world, as remote work rises and companies consider the pros and cons of permanent remote work, though it is also a common practice to index an employee's compensation according to their physical working location.
Below, we explore the median Data Science salary by city.
Source: U.S. Bureau of Labor Statistics
However, there is quite a bit of variance in how far that data scientist salary will actually go due to the cost of living of said location. Cities are often categorized into low, medium, and high cost of living locations. The rise in rent and other costs in some cities, such as San Francisco and New York, has even extended the usual "High Cost of Living" (HCOL) term to VHCOL, or Very High Cost of Living.
The Consumer Price Index, or CPI, is a "measure of the average change overtime in the prices paid by urban consumers for a market basket of consumer goods and services" as defined by the Bureau of Labor Statistics. Covering food and beverages, housing, apparel, transportation, medical care, recreation, education and communication, and other goods and services, the CPI can be used to approximate the cost of living in an area.
Notice that the cities with the highest median Data Science salaries generally also have a higher CPI.
Source: U.S. Bureau of Labor Statistics
Now when we divide each city's CPI by the median Data Science salary, we can get an approximation for how far your salary could go at each location.
Source: U.S. Bureau of Labor Statistics
Notice that high-paying areas, such as San Francisco and New York, balanced against their high costs, become the jobs where data scientists are paid the worst. Suddenly, areas like Baltimore or Denver become better paying despite the median nominal salary being among the lowest.
This doesn't mean you cannot live in the HCOL areas, but it may be harder to maintain a higher lifestyle quality. Keep these factors in mind as you consider where you would want to move for work.
Also, check out our guide on how to become a data scientist from scratch that will take you through every necessary step to become a successful data scientist.
Data Science is a rapidly expanding industry with opportunities in every industry. However, many factors can affect how you are compensated, including the company, industry, and location. At the same time, these can have practical effects on your life, such as career progression, personal interests, and quality of living. The national median of $104k doesn't mean your entry-level data science role will also pay that. Remember to take all of these factors into consideration when researching for a fair and competitive salary for a role in data science. Good luck!