Updated on 13 Sept 2022

Data Science Salaries Around the World

Aleksandra Yosifova Published on 12 Sept 2022 11 min read

Data science is one of the top 3 career choices in terms of compensation. However, we know that there can be considerable differences based on location, company type, level of experience, and so on. 

So, before choosing your career path, consider all possibilities. You don’t want to enter the field with unrealistic expectations and be disappointed. 

We at 365 Data Science are firm believers in making informed decisions. To help you out, we present a detailed overview of data science salaries across different countries, levels of education, industries, and more. 

For the sake of consistency, the comparison includes self-reported Glassdoor data only. We present all numbers in US dollars. That said, we need to be wary that a dollar in New York has a different purchasing power than a dollar in Mumbai or Eastern Europe, for example. 

Nevertheless, the numbers are more than intriguing. Read on and see for yourself or watch the video below for a concise overview of the topic. 

Data Science Salaries Around the World: Table of Contents

  1. Data Science Salaries by Country
  2. Data Science Salaries by Industry
  3. Data Science Salaries by Education
  4. Data Science Salaries by Job Title
  5. Data Science Salaries by Company Size
  6. Data Science Salaries by Years of Experience
  7. Data Science Salaries Around the World: Next Steps

Data Science Salaries by Country

Where you live has a huge impact on your earnings. To illustrate the variation in salaries between and within countries, we’ll provide the national average first, followed by the average salaries in selected cities. 

Let’s start with the old continent – Europe.

United Kingdom

Across the British market, most data science employers are based in England (91%), while 8% are in Scotland and 2% in Wales. 

As of 2022, the average data scientist salary in the UK is around \$53,904 per year. That is the base pay. With additional compensation, the number goes up to \$59,145. However, the range is huge. You could be earning anything from \$34,513 to \$84,186. 

Unsurprisingly, London makes up 55% of all data science jobs in the UK. The median data scientist salary in London is \$59,757 per year – up to \$65,925 with additional pay. 

Germany

Germany’s strong economy allowed it to surpass Britain in terms of data science salaries. The average national wage in 2022 is \$65,564 per year (additional compensation included). The lowest annual pay reported on Glassdoor is \$49,633 and the highest is \$80,031. 

Someone working as a data scientist in Munich can expect a median base salary of \$67,500 per year. The average additional compensation is \$5,347 per year, resulting in a total average pay of \$72,847. 

According to recent research, the IT market in Germany is expected to reach \$129 billion by 2025. This will create a growing demand for tech workers and data professionals. In turn, the salaries are likely to increase too. 

Switzerland

At the same time, Switzerland stands out as one of the countries with the best salaries in Europe. The average data scientist salary there is \$120,114 per year (additional pay included). Based on various factors, this number could range from \$89,806 to \$136,578 per year. 

A data scientist in Geneva will take home \$108,158 annually, plus an additional cash compensation of \$9,938. This results in a total salary of \$118,096 per year. That’s a considerable difference compared to the UK and Germany, right? 

Eastern Europe

So far, the salaries we discussed are exceptionally high. But, of course, there are countries that have some significant catching up to do. 

For example, the average data science salary in Romania is \$35,042 per year. As in the previous examples, this number is slightly higher in the capital. Data scientists in Bucharest earn around \$43,948 per year. The annual earnings range between \$8,917 and \$41,413. 

In Bulgaria, the average data scientist salary is \$30,968 per year, and in Sofia\$30,057 per year. The lowest reported annual earnings are \$19,200, while the highest is around \$36,919. 

This is the lowest amount in the European Union. Over the next few years, the strong development of the startup ecosystem in Eastern Europe and the advent of successful AI startups, such as the Romanian UiPath, may help this region match up with Western Europe. Who knows? 

But hold on for a second. 

Before making any conclusions, note that the number of salary reports on Glassdoor differs greatly between countries. In this case, it is possible that the smaller sample of data science salaries in Bulgaria and Romania has biased the results. 

Still, this presents an interesting trend, which is definitely worth considering when you’re choosing a career path. Just remember to take into account other factors, too, like the standard of living in each country and the purchasing power of the salary in question. 

Now, let’s move on to other regions. 

Africa

How much do data scientists make in Africa? 

As it turns out, Easter Europe is not the only region that needs to catch up. 

If you work as a data scientist in Cairo, you can expect to receive around \$15,940 per year. The same amount is reported for the entire Egypt area. The lowest annual base pay in Egypt, according to Glassdoor, is \$3,113, while the highest is \$15,006. 

This is marginally lower than the earnings in Romania and Bulgaria. Is that the case in Africa in general? 

Let’s take a look at another country to find out. 

The average data science salary in South Africa is \$34,184 per year, with a range between \$20,100 and \$54,549 per year. Someone working in the same position in Cape Town will make around \$35,123 yearly. While that’s double the amount in Egypt, it’s much smaller than the salaries in the Western European countries above. 

Asia

Perhaps unsurprisingly, the range of salaries in Asia is huge. We’ll focus on a few notable examples to illustrate that. 

Let’s start with India – one of the global powers in the data science world. The entry of many US companies has increased data science salaries a bit in recent years. Yet, compared to most other regions on our list, they are close to nothing. 

The average annual pay in India is \$16,285, with a range from \$5,060 to \$31,191. The number is negligibly lower in New Delhi \$16,023, and more noticeably so in Mumbai\$15,231. Lastly, someone working in Hyderabad would receive a mean salary of \$15,353. 

As you can see, the variation within the country isn’t that big. But like we said, the situation looks completely different in other Asian regions. 

How much do data scientists make in Japan, for example? 

The annual pay varies between \$25,304 and \$75,902, with a median value of \$53,965 (bonuses included). The average salary in Tokyo is \$53,732. While this can’t compare to the data science salaries in Switzerland, it’s still in the higher earnings bracket. 

Australia

Not too far from India in terms of distance, but quite far in terms of pay, is the Land Down Under. Australian data scientists receive a mean income of \$87,218 per year, with a range from \$55,035 to \$100,637. 

There are very few reports of data science salaries in Canberra, but the average amount is \$67,091. The median pay is considerably higher in Sydney, at \$83,864 per year, and in Melbourne, at \$80,509. 

That’s significantly more than the previous few examples, isn’t it? It’s only surpassed by Switzerland so far. Now, let’s see how the Land of Opportunity stacks up against the Land Down Under. 

North America

Finally, we’ll take a look at North America. 

If you want to work as a data scientist in Toronto, Canada, you can expect around \$75,152 per year. The national average salary for this position is \$73,447, with the lowest reported amount being \$47,362 and the highest – \$93,532. 

As expected, US salaries are the highest, with a total annual pay of \$122,480. The lowest reported earnings are \$67,550 and the highest are \$148,493 per year. 

Let’s break this down further. 

The average data scientist salary in New York is \$122,321 per year. That’s more than the highest payment we’ve seen on this list so far – in Switzerland. But the earnings are even bigger in San Francisco, with an annual base pay of \$146,100, plus \$29,756 as additional compensation. This amounts to a total of \$175,856. 

Let’s see if anything can surpass this. 

The average data scientist salary in Boston is \$129,449 in total. That’s still pretty high, right? Even slightly more than New York. 

Next, we’ll take a look at the average data scientist salary in Chicago. Someone working there could expect to earn around \$113,700 per year in total. 

We turn to the West to see the average data scientist salary in Denver, Colorado. The total pay is \$127,779 per year (\$117,890 base pay and \$9,889 additional compensation). Again, that’s pretty high, but not the highest we’ve seen, right? 

Ladies and gentlemen, we have a winner – San Francisco. But, as you can see, data scientists earn big money pretty much everywhere in the US. 

Still, the United States Bureau of Labor Statistics expects that the demand for trained data scientists will continue to surge, resulting in a 27.9% rise in employment by 2026. The increased demand leads to a lack of trained professionals, which, in turn, could drive salaries even higher. 

So, if you’re still wondering whether to pursue a career in data science, this is your sign to go for it. You can start with our Introduction to Data and Data Science course to get a feel of the field. 

Take the first steps toward a successful career in Geneva or San Francisco, or Tokyo – the world is your oyster. Of course, as we said, location isn’t the only factor determining your salary. 

Let’s get back to the list so you can choose the right path before you dive in. 

Data Science Salaries by Industry

Apart from location, industry is the other major factor influencing the size of salaries. In this section, we’ll focus on the average earnings of data science specialists in different sectors. 

For simplicity, we will report all amounts in US dollars and compare them to the national average. As we remember, the average annual pay of data scientists in the US is \$122,480. 

So, let’s begin with some of the highest-paying industries: 

  • Government and administration – \$138,212 
  • Information technology – \$135,760 
  • Legal – \$132,010 
  • Arts, entertainment, and recreation –\$129,843 

The average salary of a data scientist in the financial services sector is \$128,317 per year. In healthcare, you can expect to make a total of \$125,558, and in the personal consumer services industry – \$124,281. 

In the midrange are the following sectors: 

  • Agriculture – \$123,798 
  • Management and consulting – \$123,852 
  • Aerospace and defense – \$122,370  
  • Education – \$121,919 

Most industries fall in this group – insurance, manufacturing, media and communications, and so on. Finally, we present a few sectors in the lower earnings bracket. Well, low by the high expectations we set with the previous examples. 

  • Pharmaceuticals and biotechnology – \$119,366 
  • Energy, mining, and utilities – \$114,659 
  • Construction, repair, and maintenance services –\$111,741 
  • Telecom – \$107,080 

At the bottom of our list is marketing, with an average data scientist salary of \$92,425 per year. 

Of course, this comparison is by no means exhaustive. There are numerous sectors out there, and the variation within them is just as big as it is between them. Still, this gives us a good idea of the range we can expect in different sectors. 

Moving on to the next factor. 

Data Science Salaries by Education

The trend we’ll see here won’t surprise you – employees with more advanced degrees tend to earn more. But how significant is this difference exactly? 

Our research shows that over 90% of data scientists have some level of higher education. And while more employers become open to hiring professionals with bachelor’s degrees, those with Master’s still hold the majority. In addition, someone with a PhD will likely have a smaller salary than someone with a Master’s in data science. 

Is that the case only in data science or in other related fields as well? 

The following example from data analytics will provide an answer to that question. 

A 2019 study by Forbes powered by Statista revealed that 71% of Predictive Analytics Professionals have a Master’s degree and 15% have a PhD. There is also a positive correlation between the level of education and people’s earnings. In other words, those with a PhD tend to earn more than those with a Master’s, and so on. 

That said, simply having a Master’s in data analytics won’t secure you a bigger salary. As we saw in the numerous examples above, your pay depends on a plethora of factors. 

Sure, the starting salary of a data scientist with a PhD might be higher than that of someone holding only a bachelor’s degree. However, the years of experience, industry, company size, and country will have just as strong, if not stronger, impact on earnings. 

Holding an advanced degree shows employers that you have the background they are looking for. Choosing a good data science Bachelor’s or Master’s degree will give you a head start. Still, from there on, your salary will depend on your performance. 

That said, education plays a bigger role in some positions than others. In management roles, for example, employers value experience over education. As you’ll see in the examples below, this is also reflected in the salaries. 

Data Science Salaries by Job Title

We covered a wide range of factors influencing the earnings of data scientists. To get an idea of the variation within the field, we’ll take a look at a few data-related roles. 

We’ll start with the more basic and end with the advanced positions. 

The job of a data researcher is to collect, manage, and interpret data in a given organization. The aim is to discover patterns and gather useful insights for the company. Since it doesn’t involve the use of complex tools and coding typical for data science positions, the median data researcher salary in the US is just \$78,068 per year. 

Data analyst is often used interchangeably with data researcher, especially in recruitment. However, by definition, it refers to the analytics part of the job, and not the collection and management of data. Since this position involves more complex analysis, specialists in the field tend to have slightly bigger earnings than researchers. The average data analyst salary is \$99,252. 

That is nothing compared to what data engineers earn, though. The median data scientist engineer salary is \$141,019 per year. Their job involves building systems for collecting and managing data, as well as preparing raw data for analysis. In other words, their work precedes and complements the work of data scientists. 

Moving on to the next role. 

In bigger companies, there may be teams of people in different data-related roles. In those cases, someone needs to coordinate and oversee their work. That’s what the data science manager role entails. 

Along with the main duties of a data scientist, they are responsible for directing a team and communicating with other departments to create a unified data science strategy. In exchange, the average data science manager receives a \$134,051 salary. 

Another responsible and highly paid role is that of a principal data scientist. Someone in this position would need business acumen and about a decade of experience on top of their excellent data science skills. An accurate reflection of the high level of responsibility this role involves, the median total annual salary of a principal data scientist is \$166,485. 

Is your head spinning? We’re not done yet. 

Data Science Salaries by Company Size

The salary you earn also depends on the organization you work for. 

According to Glassdoor, bigger companies would pay a data scientist 19.5% more than smaller businesses. And, as expected, the big names out there have the highest pay rates. 

Here’s a list of the top 10 highest paying companies: 

  • Earnest – \$214,490  
  • LendingClub – \$212,626  
  • Twitter – \$210,981  
  • Spokeo – \$210,680  
  • Mozilla – \$209,390  
  • Aviso – \$208,970  
  • Yahoo – \$206,590  
  • eBay – \$206,097  
  • Microsoft – \$205,334  
  • Xevo – \$205,224 

You may be surprised that none of the FAANG companies appear on that list. That doesn’t mean they don’t pay well, of course. It just means they are not among the top 10 at the time of writing. Besides, many factors influence that classification. 

The numbers above reflect the median total pay regardless of years of experience extracted from user reports on Glassdoor. If we only take entry-level salaries, for example, entirely different companies may appear on the list. 

Speaking of experience, let’s see how earnings changes with time. 

Data Science Salaries by Years of Experience

The average entry-level data scientist salary is \$111,685 per year. Naturally, as you progress with your career, your earnings will grow. 

Below are Glassdoor’s estimates of the median annual total pay for data scientists in the US based on years of experience: 

  • 0-1 years: \$124,485 
  • 1-3 years: \$131,398 
  • 4-6 years: \$138,697 
  • 7-9 years: \$143,851 
  • 10-14 years: \$152,797 
  • 15+ years: \$163,489 

That’s a great motivation booster and an excellent example of the importance of persistence. We at 365 Data Science are firm believers in continuous learning and development. As such, we offer advanced specialization trainings. Courses like Data-Driven Business Growth can help you progress to the next level in your career. 

Data Science Salaries Around the World: Next Steps

This wraps up the comparison of data science salaries across different dimensions. We hope you enjoyed it and found it useful! If you’re still unsure whether this is the right job for you, you can check out our course Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process. 

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Aleksandra Yosifova

Blog author at 365 Data Science

Aleksandra is a Copywriter and Editor at 365 Data Science. She holds a bachelor’s degree in Psychology and is currently pursuing a Master’s in Cognitive Science. Thanks to her background in both research and writing, she learned how to deliver complex ideas in simple terms. She believes that knowledge empowers people and science should be accessible to all. Her passion for science communication brought her to 365 Data Science.

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