Top 10 Data Science Jobs: Which Position is Offered the Highest Salary?

 Data science jobs

Watch out for the top 10 highest-paying data science jobs for 2022. 

As data started pouring into data centers in huge numbers, it opened the door for many opportunities, especially in data science. Technological changes in the data landscape left digital transformation as the only choice. As more and more companies turn towards digitization, they seek people to fill the data science and relative positions. The demand for data science practitioners is spreading across the globe. Demand for data-related professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions. This article lists the top 10 highest-paying data science jobs for 2022. 


Data Analyst

Average salary (Per Year): US$65,558 per year

The job of the data analyst is to assign a numerical value to these important business functions so that the company’s performance can be accessed and compared over time. Data analyst jobs mostly involve looking at numbers and knowing how to use data to enable an organization to make more informed decisions.


Data Scientist

Average salary (Per Year): US$122,519

Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data, then interpret the results to create actionable plans for companies and other organizations. Remarkably, data scientists are also analytical experts who utilize their skills in both technical and social science to find trends and manage data. The actual job involves making sense of unstructured data from sources such as smart devices, social media feeds, and emails.


Data Engineers

Average salary (Per Year): US$132,571

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. Data engineers are accountable for building algorithms to help give easier access to raw data. But to do this, they need communication skills to work across departments to understand what business leaders want to gain from the company’s large datasets. Data engineers also need to understand how to optimize data retrieval and develop dashboards, reports, and other visualization for stakeholders.


Business Intelligence Analyst

Average salary (Per Year): US$100,494

Business intelligence analysts are proficient in computer programming languages, Business Intelligence (BI) tools, technologies, and systems. BI analysts determine business-critical priorities and requirements. They define KPIs (Key Performance Indicators), implement DW (Data Warehouse) strategies, and identify BI by mining big data using advanced software and tools. The BI analyst’s primary goal is to empower decision-makers with accurate, real-time, actionable insights that enhance workflow efficiency, increase productivity, strengthen market positioning, improve the competitive edge and augment customer experience.


Quantitative Analyst

Average salary (Per Year): US$129,858

A quantitative analyst or ‘quant’ is a specialist who applies mathematical and statistical methods to financial and risk management problems. Investment banks, asset managers, hedge funds, private equity firms, and insurance companies employ quantitative analysts to help identify profitable investment opportunities and manage risk.


Machine Learning Engineer

Average salary (Per Year): US$149,847

Machine learning engineers sit at the intersection of software engineering and data science. They leverage big data tools and programming frameworks to ensure that the raw data gathered from data pipelines are redefined as data science models are ready to scale as needed. Machine learning engineers focus on designing self-running software for predictive model automation. They work closely with data scientists and ensure that the models used can ingest vast amounts of real-time data for generating more accurate results.


Data Architect

Average salary (Per Year): US$133,840

Data architects are senior visionaries who translate business requirements into technical requirements and define data standards and principles. After assessing a company’s potential data source, both internal and external, data architects design a plan to integrate, centralize, protect and maintain them. Data architects are responsible for visualizing and designing an organization’s enterprise management framework.


Database Administrator

Average salary (Per Year): US$97,730

Database administrators create and maintain databases compatible with their companies’ needs. They oversee database updates, storage, security, and troubleshooting. Database administrators ensure that data remains consistent across the database, data is clearly defined, users access data concurrently and there is provision for data security and recovery control.



Average salary (Per Year): US$99,055 

Statisticians practice the science of using data to make decisions. They decide what data they need and how to collect it, design experiments, collect, analyze and interpret data, and report conclusions. Statisticians often work in teams, usually including professionals from other disciplines. They interpret data and communicate results to their clients, often with the aid of mathematical techniques and software.


Enterprise Architect

Average salary (Per Year): US$144,013

Enterprise architects are responsible to make sure that a company’s business strategy uses proper technology systems architecture to achieve its goals. Enterprise architects have an enormous degree of responsibility and report directly to the chief information officer (CIO). They need to keep up with the latest trends in technology and determine whether or not they would be the right fit for a company.