Data scientists and quantitative analysts have similar jobs: both use data and analytics to solve complex business problems. But, there are significant differences between each job. Find out more in this guide.
Here’s what we’ll cover:
The global big data market is continuing to grow at a rapid pace. According to Statista, the market will double by 2027 as more and more businesses discover a need for workers who are capable of breaking company data down into a digestible, usable format.
Quantitative analysts and data scientists fill similar roles within organizations looking to mine valuable insights from data. The main difference between the two is how they work with the information.
Let’s take a closer look at what each career involves. Understanding the differences between the two roles may help you figure out which career path suits you best.
Quantitative analysts build, design, and implement complex data models. They conduct predictive analytics for institutions like:
Building on this role, technologists like quant traders are the dominant voice on trading floors. They're analyzing trends and searching for the most profitable trades. The amount of trading spurred by computer algorithms has led to the role of trader and quant analyst merging in many trading firms.
There are three different types of quantitative analysts, or quants, found throughout various industries:
Quantitative analysts work in major financial institutions across the U.S. like New York and Chicago. Other responsibilities for quantitative analysts include:
Data scientists work with structured and unstructured data to find solutions to complex problems and business challenges for organizations. Data scientists rely on a combination of knowledge from the worlds of computer science, mathematics, and statistics.
Examples of information worked with by data scientists include sales data from past transactions, data from social media profiles, or information collected from server logs. Data scientists analyze the data and display the results as data visualizations or reports that are more easily understood by non-data scientists.
Data scientists try to come up with answers to questions presented to them by an organization, like what metrics they should be tracking or how they can improve profitability around a product line. They might review customer demographic details to get a sense of customer preferences. Doing so requires designing data modeling processes, predictive models, and algorithms to extract information relevant to the query. A company could use that feedback to find features to include in a product to appeal to those consumers.
Data scientists have helped pharmaceutical companies figure out the population of individuals most responsive to a promising new drug. Airbnb relies on data science to help it with everything from reviewing different renter demographics to predicting when housing will become available at various price points.
Below is the typical process followed by data scientists when working on solutions for businesses.
Quantitative analysts and data scientists both analyze data and use the insights to benefit an organization. In some companies, data scientists may assume responsibility for building data pipelines to pull in the information collected from a website or stats highlighting the performance of a current marketing campaign. They clean the data, analyze the information, and create data sets. Data analysts review the data sets to reveal meaningful insights and find information executives can use to make business decisions.
Here are a few of the main differences between quantitative analysts and data scientists:
Quantitative analysts often use their expertise in finance, while data scientists can be found in companies that work with artificial intelligence, database management, and machine learning. Their skillsets can differ depending on an individual’s educational background.
Many quantitative analysts who work in finance are skilled at working with numbers and have master’s degrees or higher in math and finance. Data scientists tend to be experts at using technology like Hadoop and Spark.
Skills typically found among quantitative analysts include:
Skills often found among individuals working as data scientists include:
Both roles call for soft skills like:
Once you start working as a quantitative analyst, you could eventually branch out into different positions like business analyst, operations analyst, financial analyst, or quantitative trader. If you are interested in becoming a data engineer or architect, then working in a data scientist role can put you on that path.
According to the Bureau of Labor and Statistics (BLS), people working as data scientists and in other mathematical science occupations averaged an annual salary of $98,230. Glassdoor lists the average salary of a quantitative analyst at $109,437 and that of a data scientist at $115,512.
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