11 Types of Data Science Jobs (With Responsibilities)

By Indeed Editorial Team

Published April 14, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data science is the process of gathering and analyzing data to better make decisions and understand situations. Data scientists can specialize in many areas of data science, from business intelligence to statistical analysis. If you're a data scientist or you want to become one, learning more about the various fields of data science can be beneficial. In this article, we explain what a data scientist does, explore 11 types of data science jobs and provide helpful tips you can use to become a data scientist.

Related: How To Learn Data Science (Plus 5 Jobs To Consider)

What does a data scientist do?

Data scientists use the information to solve problems and help others make informed decisions. These are some of the responsibilities that a data scientist may manage:

  • Create and implement data models

  • Coordinate with other professionals

  • Present visual data information

  • Study new data science technology

  • Synthesize data and information

Related: Top 12 Skills You Need To Become a Data Scientist

Data scientist salary and job outlook

How much a data scientist can earn may depend on factors like performance, experience and education. The national average salary for a data scientist is $103,043 per year. According to the Bureau of Labor Statistics, computer and information research scientists may expect a growth rate of 22% in their field, which is much faster than the average.

11 types of data science specializations

These are 11 types of data science specializations you can pursue:

1. Business intelligence

Business intelligence is a type of data science in which the scientist gathers and assesses information with the purpose of informing business leaders. The information they analyze during business intelligence data processes helps businesses make choices to lead them to success. For example, business intelligence data scientists may gather information about competitors' performance so business leaders can strategize.

2. Cloud computing

Cloud computing is the process of delivering services, storage and databases through the internet. Data scientists focus on gathering and analyzing data stored in databases on the cloud. They also help to store and organize information in cloud computing data storage locations.

3. Cybersecurity analysis

Cybersecurity data scientists gather information about the security of an organization's online and internal information. They assess data regarding safety to determine how easily outside sources may access information. Cybersecurity data scientists can help other cybersecurity professionals ensure an organization's information stays private and secure.

4. Data engineering

Data engineering is the process of designing and creating systems to gather and store information. They also create programs that can convert information from raw data into synthesized materials that scientists can use to draw conclusions. Engineers also make systems that provide data access to several individuals within an organization.

5. Data mining

Data scientists who focus on data mining use software to find patterns in large sets of data. They can search for correlations and anomalies to learn more about accuracy. Finding patterns can also help data miners predict outcomes and understand trends.

6. Data visualization

Data visualization experts focus on taking raw information and presenting it in a way that their colleagues can understand. They may create graphs and figures to demonstrate how data compares and what it means. For example, a data visualization expert may create a presentation to show a business leader how a new product may perform in the market.

Related: A Definitive Guide on Changing Your Career to Data Science

7. Data warehousing

Data warehousing is a form of data management. Some data scientists focus on designing and constructing data warehouses in which to store large amounts of information. Many data warehouses are useful for business intelligence professionals.

8. Machine learning

Machine learning in data science is the process of teaching robots and programs to behave in certain ways. A data scientist who specializes in machine learning uses information and algorithms to help machines understand how to react to problems and scenarios. They develop artificial intelligence that can improve operations for manufacturing and design.

9. Market data analytics

Market data analysts are scientists who focus on how products and services perform within markets. They may gather information about how events affect profits. They can also predict how new products or services may perform and help leaders prepare for what may happen in different world events.

10. Operations data analytics

This is a type of business analysis that focuses on integrating data into operation. Rather than striving just to understand data and predict patterns, an operations data analytics professional focuses on creating tools based on analyses. For example, an operations data analytics professional may suggest changes to supply chain management based on data.

11. Sector-specific

Sector-specific data scientists are those who work in a certain field and specialize in their work accordingly. This can include health care data analysts, who learn more about the population's health and trends in treatment. Another example of a sector-specific data scientist may be a social media specialist, who focuses on finding new ways to attract users to an app based on their usage time and approval ratings.

Related: Learn About Being a Data Scientist

Tips for becoming a data scientist

These are some tips for becoming a data scientist:

  • Pursue a relevant bachelor's degree: Many employers require that you have at least a bachelor's degree to get a position as a data scientist. Consider pursuing a degree in information technology, computer science, math, statistics or physics.

  • Gain experience in your desired sector: If you want to work as a social media data scientist, consider completing internships in that field, even if they don't involve data science. This can help you develop a professional network and gain more opportunities.

  • Choose a mentor to help you advance: A mentor is someone who can offer specific advice about how to advance your career. Ask someone with experience and knowledge to help mentor you in your position.

  • Determine your focus early in your career: Choosing a focus early in your career can help you develop specific skills and narrow your career path.

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