How much does a data analyst make in 2021?

Data science is one of the most promising and fascinating career niches of the 21st century. With the up-gradation of technology, data is getting more complex, which raises the demand for data science professionals.

Numerous young individuals are willing to step up for the data science profession, however, most of them are curious about the money they can make as data scientists. Being a part of this money-centric era, everyone desires to pursue a career in which they can become financially stable.

Considering this, here we have compiled all the important information related to the data scientist salary across the globe.

Here you will get an insight into the average salary packages for different profile titles related to data science including entry level data analyst salary, data engineer salary, statisticians salary, big data engineer salary, and research scientist salary.

We have elaborated on all the Data Scientist salary details by answering the following questions in this blog:

Go through our complete guide on data scientist resume building to get a clear idea of what goes into creating the perfect recruiters friendly resume. Moreover, we have also crafted ready-to-use one-liner bullet points that can be used while building your data science resume for better results.

Check out the bullet points for similar profiles here:

If you want to get a data scientist job, check out Hiration’s AI-powered Online Resume Builder that can help you in building a job-winning resume for your targeted job profile.

What is a Data Scientist


The data scientist is a professional that gathers and analyzes huge sets of unstructured and unorganized data.

Proficient in executing computer science, statistical, and mathematical operations, Data Scientists interpret the results of data processing, modeling, and analysis to make actionable business plans.

Roles & Responsibilities of a Data Scientist

Let us see some roles and responsibilities of a data scientist:

  • Collect, analyze, and transform structured as well as unstructured data to get fruitful information
  • Develop new data-analytics solutions that can help in the exponential growth of business
  • Communicate with internal teams and external clients for a deep business model understanding and provide analytical solutions
  • Prepare ETL scripts as well as data pipeline processes and integrate them in the cloud-based-products and IoT

How Much Do Data Scientists Make?


Data Scientist is one of the highest salaried career options in this generation. However, getting all the data science skills is not a piece of cake for every individual. You will have to spend a lot of time learning every data science skill and grow eventually.

Deeper knowledge and diverse skills in data science will make you an asset for which many recruiters will pay a handsome data analytics salary. Here you can see the 6 main factors that affect data scientist salaries:

  • Job Title
  • Experience
  • Company Size
  • Industry
  • Education
  • Region

Effect of Job Title on Data Scientist Salary


Job title plays a vital role in defining the salary of a data science professional. Additionally, it is also proven that the data scientist salary gets higher if he/she is engaged in a managerial task like identifying business issues and solving them with analytics, team leading, or client communication.

Take a look at some data science job titles along with their salary range:

Job Title Data Analyst Average Salary Range
Data Scientist USD 85,000-170,000
Data Analyst USD 50,000-110,000
Data Science/Analytics Manager USD 90,000-140,000
Big Data Engineer USD 70,000-165,000

Data Scientist Salary Based on Experience

Just like any other profession, an experienced senior data scientist gets a better salary than a junior or entry-level data scientist. A data scientist gets an average salary hike of USD 2,000-2,500 with an yearly upgrade.

data-scientist-salary-by-experience

Image Source: Burtch Works

The above-given graphical representation has been taken from a recent report of Burtch Works. It is showcasing the effect of experience on a data scientist salary. The report further highlights the latest data scientist salary trends based on experience that can be seen in the table provided here:

Experience Level Data Scientist Average Salary Range
Entry Level Data Scientist USD 95,000
Mid Level Data Scientist USD 130,000-195,000
Experienced Data Scientist USD 165,000-250,000

Data Scientist Salary Based on Company Size & Industry

Though a data scientist can benefit almost every business working beneath this sky, three main industries pay the highest median salaries for a data science professional:

  • Banking & Finance
  • Search & Social Networking
  • Cloud Services, Hosting, & CDN

According to a report by O’Reilly in 2016, only 12% of data scientists were working in these industries. Approximately 30% of data science professionals were working in either software (SAAS, web, and mobile) or a consulting firm.

The highest-paid data scientists unsurprisingly work in leading tech companies. Some of them can be seen in the table given below:

Company Name Data Scientist Average Salary
Apple USD 145,974
Google USD 152,856
PayPal USD 132,909
Twitter USD 135,360
Facebook USD 134,715
Microsoft USD 123,328
Airbnb USD 127,852

data-scientist-salary-by-company-size

Image Source: O'Reilly 2016 Report

Considering the company size, the same report states that bigger companies pay more as compared to smaller companies. For example, a data scientist will earn a higher salary in a company having 10,000 employees as compared to a company having 1,000 employees for the same role.

Data Science Salary Based on Educational Qualifications


Companies find it difficult to hire a data scientist to fill in all of their requirements. An individual must have the perfect combination of education and skills as per the requirements of an organization to get selected.

Generally, data scientists who code eight to ten hours a week get a higher salary package compared to the data scientists who do not code at all. Here are some technical skills that can affect your senior data scientist salary:

  • Hadoop
  • Apache Hadoop
  • Machine Learning
  • Python
  • Data Mining
  • Big Data Analytics
  • Data Analysis
  • SaaS
  • SQL
  • Data Modeling

A data scientist must be well aware of digital tools including open-source, data visualization, and cloud computing. Moreover, data scientists should know how to use these tools for getting actionable insights to improve organizational business.

In terms of education, you need to have advanced degrees in quantitative disciplines like applied mathematics, data science, computer science, statistics, economics, operations research, or engineering to be a data scientist. Apart from this, you can also get a data science certification to make your job-hunting more effective.

Data Science Salary in the United States


Data Scientist salaries also depend on the region where an organization is situated. According to the report shared by O'Reilly, the data scientists in California get the highest salaries in the entire United States. The second position has been acquired by the Pacific Northwest in the country.

You can see a graphical representation of this section in the image shown here:

data-scientist-salary-by-region

Image Source: O'Reilly 2016 Report

Data Scientist Salary in Top 5 Countries

Data scientist salary also depends on the development rate of a country i.e. developed countries pay higher salaries and have more work for a data scientist compared to a developing country.

The below-given table elaborates on the top 5 countries with the highest data science salaries in the world:

Country Data Scientist Average Salary
United States of America USD 120,000 (Annually)
Australia AUD 75,233-121,578 (Annually)
Israel USD 88,000 (Annually)
Canada USD 77,870-17,750 (Annually)
Germany EUR 2,740-9,470 (Monthly)

Challenges in the Data Science Career

Being a data scientist, you will encounter various hurdles to meet the results required by your organization. You need to get past these hurdles and prove your worth to grow efficiently as a data scientist.

Here we are showcasing 5 major hurdles that might come in your way to become a successful data scientist:

Data Preparation

Every data scientist has to clean and prepare data before analyzing it. A data scientist usually spends 80% of the time on this task and terms it as the most difficult task of their role.

To resolve this time-consuming issue, you can use AI-enabled data science tools such as Augmented Analytics and Auto Feature Engineering. These models will make you more productive as a data scientist.

Diverse Data Sources

Data scientists need data from numerous data sources to get fruitful results for the organization. Using different applications and technologies will make them search and enter data manually, which is very time-consuming and can cause errors.

As a solution, your organization can use a platform connected to these data sources. This platform will not only extract data but also store it in an appropriate format to use effectively. This methodology will save a lot of your time and effort.

Data Protection

Currently, hundreds of businesses are moving to cloud infrastructure. This transformation has woken up cyber threats on a larger scale. These threats can cause harm to confidential information and can also cause organizational failure.

To get rid of this issue, you can use security platforms powered by machine learning technology along with additional security measures. These platforms will adhere to data protection and will also save organizational time and money.

Business Issue Analysis

A data scientist should analyze the business first to understand the main goal and business challenges. Jumping into data analysis without identifying the business requirements will not be a good approach for you as a data scientist.

It is highly recommended that you consult the stakeholders and prerequisites to prepare a checklist of required results before starting data analysis. This checklist will help you in defining the priorities of your future operations.

Stakeholder Communication (Non-Technical)

A data scientist must have the ability to communicate with a non-technical audience effectively. Generally, the CEO or non-tech stakeholders do not understand the complexity of data science.

Hence, to get a clear image of their requirements, you must learn to explain these technical concepts to the non-tech audience. You can use concepts such as ‘data storytelling’ to resolve this issue.

Hiration Services

To get a data science job, you will need an impeccable data science resume apart from the educational degrees and skills.

Hiration’s AI-powered Online Resume Builder will help you in building a professional and impactful data science resume.

Check out some additional services offered by hiration to help you in consolidating your professional position as a data scientist:

Key Takeaways

Data science is undoubtedly a futuristic career niche that can make you achieve greater heights. You can see all the essential information related to a data scientist salary in the above-given blog.

However, we advise you to choose career paths according to your interest rather than the money it can generate. In case you need any assistance in getting a data scientist job, contact us at team@hiration.com. Our experts will be highly obliged to help you in getting a job.