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Quantitative Analyst vs. Data Scientist: What’s the Difference?
Data Science

Quantitative Analyst vs. Data Scientist: What’s the Difference?

5 minute read | July 8, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

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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.

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. Understanding the differences between the two roles may help you figure out which career path suits you best. Find out more in this guide.

What Is a Quantitative Analyst?

What Is a Quantitative Analyst

Quantitative analysts build, design, and implement complex data models. They conduct predictive analytics for institutions like:

  • Investment banks
  • Commercial banks
  • Insurance companies
  • Hedge funds
  • Management consultant firms‌

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.

What Does a Quantitative Analyst Do?

There are three different types of quantitative analysts, or quants, found throughout various industries:

  • Front-office quants. Front-office quants work directly with salespeople and traders on the floor. They search for the most profitable trades and look for ways to mitigate risk for their employers.
  • Back-office quants. Back-office quants work behind the scenes doing research and validating data models.
  • Mid-office quants. Mid-office quants review assets and different markets to evaluate the potential risk they present for a firm. Many insurance companies and banks employ quantitative analysts for that purpose.‌

Quantitative analysts work in major financial institutions across the U.S. like New York and Chicago. Other responsibilities for quantitative analysts include:

  • Using analytical tools and software to develop and implement quantitative models
  • Performing statistical analysis like loan pricing and default risk modeling
  • Testing new data and data models and analytic programs
  • Working with data engineers, mathematicians, and other professionals to develop more effective data analysis strategies
  • Interpreting and presenting data results to business stakeholders and senior executives‌

What Is a Data Scientist?

What is a data scientist

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.

What Does a Data Scientist Do?

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.

  • Research the use case and figure out how they can come up with ways to provide the most value to a company
  • Collect the information needed to support the project
  • Create statistical models to analyze for relevant information and make predictions
  • Break the information down into data visualizations like a dashboard or heatmaps to help executives and other business stakeholders understand the data
  • Figure out how to integrate the data science project into current business operations‌

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What Is the Difference Between a Quantitative Analyst and a Data Scientist?

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 tend to focus on more specialized problems, especially around finance
  • Data scientists rely more heavily on their coding skills to build frameworks and automation systems
  • Data scientists typically work on a broader array of projects
  • Data scientists rely more on automation and machine learning methodologies‌

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:

  • Quantitative or mathematical finance
  • Statistical knowledge
  • Mathematical modeling
  • Big Data modeling
  • Financial modeling
  • Programming (C++, SQL, SAS, R, Python)
  • Data analytics
  • Linear algebra
  • Probability and statistics
  • Portfolio theory
  • Credit-risk models‌

Skills often found among individuals working as data scientists include:

  • Risk analytics
  • Knowledge of Big Data platforms (MongoDB, Microsoft Azure, Oracle, etc.)
  • Data structures
  • Data visualization and reporting (Tableau, SAS, R libraries)
  • Data cleaning
  • Data mining‌

Both roles call for soft skills like:

  • Effective communication techniques (written and verbal)
  • Problem-solving skills
  • Ability to conduct in-depth research
  • Aptitude for working cohesively with others in technical and non-technical roles‌

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.

Are Quantitative Analysts and Data Scientists Paid the Same?

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.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.