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Quantitative Analyst vs. Data Scientist: What Are the Differences?

Learn about the two careers and review some of the similarities and differences between them.

Data science and quantitative analysis are two of the most in-demand skills in the job market today. But what exactly do these terms mean? And what are the differences between these two roles?

In this article, we’ll explore the differences between quantitative analyst and data scientist, and help you decide which one is right for you. We’ll also provide some tips on how to break into these exciting and lucrative careers.

What is a Quantitative Analyst?

Quantitative Analysts use mathematical and statistical modeling to analyze data and solve problems in business, finance and other industries. They develop algorithms to automate tasks and processes. They also build financial models to forecast market trends and assess investment risks. Quantitative Analysts typically have a bachelor’s degree in mathematics, statistics, economics or a related field. They use their skills in critical thinking, problem solving and attention to detail to find solutions to complex problems.

What is a Data Scientist?

Data Scientists collect and analyze data to help companies make better business decisions. They use their findings to develop new products, services and marketing campaigns. Data Scientists typically have a background in computer science, mathematics or statistics. They use their skills to clean and organize data, then they use statistical methods and machine learning algorithms to find patterns and trends. Data Scientists present their findings to company leaders in reports and visualizations. They also develop prototypes and applications based on their findings.

Quantitative Analyst vs. Data Scientist

Here are the main differences between a quantitative analyst and a data scientist.

Job Duties

Quantitative analysts and data scientists fulfill different roles within an organization. A quantitative analyst uses their expertise to perform in-depth analysis of large sets of data. They use statistical methods to identify patterns and make predictions that assist the company in making strategic business decisions. Data scientists work more closely with other departments, such as IT and marketing, to apply the findings from the quantitative analyst’s research and develop solutions for the company to implement. The main difference is that a quantitative analyst performs the analysis, while a data scientist interprets and applies the findings.

Job Requirements

A quantitative analyst typically needs to have a bachelor’s degree in mathematics, statistics or another related field. A master’s degree or doctorate may be preferred by some employers, but it is not always required. Additionally, many quantitative analysts pursue certifications through the Institute for Quantitative Research in Finance (IQRF) or the Financial Modeling and Analysis Program (FMAP). These programs offer training that covers topics like financial modeling, risk management and investment analysis.

Data scientists also need to have a strong background in mathematics and statistics. In addition, they should be proficient in programming languages like Python, R and SQL. Data scientists often have a bachelor’s or master’s degree in computer science, engineering or another related field. Some data scientists also pursue a Ph.D.

Work Environment

Quantitative analysts typically work in an office setting, often for a large company. They may also travel to meet with clients or attend conferences and events related to their field. Data scientists can also work in an office environment but are more likely to work for smaller companies that require them to travel frequently.

Data scientists may also work remotely from home offices or other locations. This is because data science requires access to computing resources and software, which means they can complete much of their work independently.


Both quantitative analysts and data scientists use analytical skills to examine data and draw conclusions from it. They both need to be able to understand complex problems and develop models to solve them. They also both need to have strong math skills.

However, quantitative analysts tend to focus more on financial data, while data scientists often work with a wider range of data sets. Data scientists also benefit from having computer programming skills, as they often work with large data sets that require automated analysis. Additionally, data scientists typically need to be able to communicate their findings to non-technical audiences, so strong communication skills are important in this role.


Quantitative analysts and data scientists both work with data, but they have different roles. Quantitative analysts focus on financial data, while data scientists work with all types of data. Quantitative analysts earn an average salary of $113,816 per year, while data scientists earn an average salary of $118,822 per year.


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