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Is Data Science a Good Career Choice? Exploring Opportunities and Growth Potential

Table of Contents

  • [toc headings="h2" title="Table of Contents"] According to statistics from the Bureau of Labor Statistics, employment in data science is projected to grow at 11 times the average rate through 2032, at a total growth rate of 35%. Based on these figures, there are certainly plenty of data science jobs to go around, but that doesn't necessarily mean it's the right career choice for everyone. Let's take a closer look at the career opportunities for a data scientist to help you decide if this in-demand profession is the right fit for you.

  • Overview of the data science career path

  • Data science is an area of computer science focused on analysis. These professionals gather data and interpret it in order to find solutions to complex problems related to a range of industries, from marketing and business intelligence to insurance, finance, healthcare, and defense.  People who excel as data scientists tend to have a few key skills. Their primary focus is on working with large datasets, which they manipulate and interpret through the use of algorithms and modeling to find patterns. These same data science skills can be employed for training algorithms used in artificial intelligence and machine learning. Since it's a technology-focused career, most data scientists also need to have experience with programming languages like Python and R, as well as overall knowledge of computing best practices, tools, and software.  To perform effective analytics on large volumes of data, data scientists also need to have a strong background in statistics and mathematics. Other things that are key for careers in this field include knowledge of business analytics, data analytics, and soft skills like decision-making, research, and communication skills.  The main difference between a data scientist and similar roles like data analyst is the breadth and complexity of their skill sets. Data scientists have skills that span multiple disciplines, combining strong knowledge of the business world and best practices with expertise in information technology. This gives them the flexibility to deliver meaningful insights, not just based on the numbers and facts, but on their understanding of the broader business landscape, Individuals who thrive in data analysis are those with an inquisitive mind and an ability to solve complex problems through logic, information, and research. They tend to enter the field with at least a Bachelor's degree in a field like computer science, information technology, applied mathematics, or statistics. Once a professional has this training, there are a wide range of different roles they can take, depending on their specific interests and skills, up to executive leadership roles like Chief Data Officer or Chief Analytics Officer.  

  • Job opportunities in data science

  • As you saw from the statistics in the introduction, companies today have a growing need for professionals with expertise in data analytics. Data from Deloitte indicates there will be a shortage of roughly 250,000 data scientists by 2024, and while new technologies like low-code platforms and AI can automate some of these tasks, businesses across fields still have a growing need for the insights and value a data scientist provides.  One reason there is so much demand for data scientists is that they can fill a variety of positions. Here are some of the most common roles for these professionals that you're likely to see on the U.S. job market today. 

  • Data engineer

  • Data engineers are the data professionals who build and test big data ecosystems for organizations. They are often also responsible for updating and improving existing systems and databases to improve their efficiency and functionality. The main distinctive feature of this role is that engineers are focused on hardware and systems rather than specifically analysis of data.  Key skills of data engineers:

    • Experience with programming languages like R, Ruby, Java, and C++
    • Experience working with ETL tools and data APIs
    • Background in data engineering
    Common responsibilities of data engineers:
    • Build and maintain databases and data management systems
    • Collect and manage data
    • Analyze data to find patterns and forecast trends
    • Report to leadership and stakeholders based on findings

  • Data architect

  • Like data engineers, architects are on the planning and system side of data analysis. They plan the strategy and management of databases to ensure they're secure, centralized, and easily integrated into other business systems. This tends to be a more senior role, with professionals working for a few years as a data analyst before taking this type of position. Key skills of data architects:

    • Experience with programming languages like SQL, XML, and Spark
    • Experience with data warehousing, data modeling, and ETL
    • Experience with cloud computing design, architectures, and data lakes
    • Expertise in Hadoop technologies such as Hive, Pig, or MapReduce
    Common responsibilities of data architects:
    • Develop and implement the overall data strategy of the organization
    • Plan and manage the end-to-end data architecture
    • Maintain and optimize the security and efficiency of databases
    • Audit data management system performance to identify areas for improvement

  • Database administrator

  • A database administrator oversees the storage of a company's data and controls who has access to it. They work closely with cybersecurity teams and need a firm command of information security concepts like access control and data recovery. This role may also be responsible for developing databases in smaller companies. Key skills of database administrators:

    • Knowledge of database management systems and query languages like SQL
    • Experience with data backup and recovery
    • Knowledge of data standardization policies and practices
    Common responsibilities of database administrators:
    • Install, configure, and maintain database management systems and software
    • Monitor the performance of and access to databases
    • Perform data archiving, backup, and recovery
    • Develop and implement standards for data management

  • Statistician

  • Where the above roles are concerned with the systems and processes surrounding data, statisticians focus on deriving insights from the data itself. They use mathematical concepts and techniques to interpret data sets and use that knowledge to solve problems in the real world. This is a skill needed in a range of industries, including insurance, healthcare, marketing, engineering, and government. Key skills of statisticians:

    • Strong logical and mathematical reasoning skills
    • Experience with data mining and machine learning technologies
    • Experience with a variety of database systems
    Common responsibilities of statisticians:
    • Gather relevant data and design data collection systems and processes
    • Analyze data to predict trends and relationships using statistical methodologies
    • Prepare reports on findings and communicate them to coworkers and leadership

  • Business analyst

  • This role differs slightly from other jobs in data science because it's more focused on deriving actionable business insights than on the data itself. That said, they still work with large volumes of data, and need to have the same in-depth understanding of data modeling, visualization, and analysis techniques. They often serve as the go-between for the IT and decision-making areas of an organization.  Key skills of business analysts:

    • Experience with data modeling and data visualization tools
    • Thorough understanding of business practices and principles
    • Strong knowledge of math and statistics
    Common responsibilities of business analysts:
    • Apply data insights to improve existing business processes
    • Develop and implement new technology and systems
    • Perform detailed analysis of business problems, opportunities, solutions, and forecasting

  • Machine learning engineer

  • This is among the most in-demand roles in data science today, as more organizations adopt AI and other automation technologies. Machine learning engineers use advanced math and statistics concepts to work with big data, allowing businesses to gain new understandings of their customers, competitors, and broader industry. Several years of experience in a software engineering or data science role is typically a prerequisite for these positions.  Key skills of machine learning engineers:

    • Experience building data pipelines and performing A/B testing
    • Experience with machine learning libraries and frameworks
    • Thorough knowledge of SQL, REST APIs, and machine learning algorithms
    Common responsibilities of machine learning engineers:
    • Design, train, and test machine learning systems and algorithms
    • Develop software, applications, and other products utilizing machine learning systems
    • Model and visualize data to derive meaningful insights

  • Typical salary and compensation for data scientists

  • Salaries across data science roles tend to be well above the national average. In part, this is because these services are in such high-demand, and tend to be critical to the overall performance of businesses. Because of this, employers are willing to pay a premium to hire these important employees who will help them make better decisions.  Entry-level roles in data science include titles like Junior Data Analyst or Junior Data Scientist. Even at this early career stage, the average salary on Glassdoor starts at a base pay of $58,000 a year. Mid-career data scientists can earn anywhere between $81,000 and $148,000 per year, with an average salary of around $111,000. More specialized and advanced roles within data science earn similarly high compensation. The current median salary rates for the roles described above in 2023 are:

  • Advantages of pursuing a career in data science

  • The combination of high pay and ample job opportunities makes data science a very enticing career for those who enjoy math, numbers, and statistics. There are a number of other factors that make this career path very appealing, as well, such as:

    • Versatility and range of work. Data science is a great career path if you like solving new problems and facing new challenges in your day-to-day work. The wide range of career paths and job options in this field give professionals a lot of variety when it comes to choosing the type of work they do. 
    • High job satisfaction. In a survey of current data scientists, 81% of respondents ranked their workplace happiness as a 4-5 on a 5-point scale. If you like doing interesting work that provides real value to your company, this is a role that will bring you a lot of satisfaction. 
    • Flexibility. Spiceworks listed data scientist as one of its top 5 remote tech jobs, with roughly 30% of job opportunities offered remotely. Even when you're expected to be in the office, a data scientist's work is largely independent, and these professionals enjoy a high level of workplace flexibility compared to other fields. 

  • Challenges and risks of the data science field

  • Of course, no career path is perfect. Along with the many benefits of working as a data scientist, there are some challenges you'll face in this field. One of the biggest challenges that these professionals face is the fact that data science is constantly evolving. Keeping your skills current requires continuous learning and new skill development. There are also so many different aspects of data science that most professionals are never able to master the full breadth of the field.  Many areas of data science also have a fairly steep learning curve, requiring professionals to learn complex concepts and highly-specialized topics on an ongoing basis. This can be exciting for people who like a challenge, but it can also be overwhelming. The specific roles within data science can also often be blurred, especially in teams that are struggling to hire enough data scientists for their business needs. Some find the lack of clearly defined expectations and responsibilities to be the most stressful aspect of this job.  That learning curve can also give data science a high barrier to entry. A Bachelor's degree is usually just the minimum requirement, and many professionals in this field also have a Master's degree, as well as several independent professional certifications. Complicating matters further, many entry-level roles for data scientists are prime candidates for automation and AI. Considering that many employers look for several years of experience when hiring candidates for more advanced roles, this can make it a challenging field to break into. 

  • So is data science a good career?

  • Data science offers numerous desirable career paths for the right kind of professional. If you love math, have a background in computer science, and want a job that will offer new challenges and learning opportunities on a regular basis, then data science can be an ideal fit. 

According to statistics from the Bureau of Labor Statistics, employment in data science is projected to grow at 11 times the average rate through 2032, at a total growth rate of 35%. Based on these figures, there are certainly plenty of data science jobs to go around, but that doesn’t necessarily mean it’s the right career choice for everyone. Let’s take a closer look at the career opportunities for a data scientist to help you decide if this in-demand profession is the right fit for you.

Overview of the data science career path

Data science is an area of computer science focused on analysis. These professionals gather data and interpret it in order to find solutions to complex problems related to a range of industries, from marketing and business intelligence to insurance, finance, healthcare, and defense. 

People who excel as data scientists tend to have a few key skills. Their primary focus is on working with large datasets, which they manipulate and interpret through the use of algorithms and modeling to find patterns. These same data science skills can be employed for training algorithms used in artificial intelligence and machine learning. Since it’s a technology-focused career, most data scientists also need to have experience with programming languages like Python and R, as well as overall knowledge of computing best practices, tools, and software. 

To perform effective analytics on large volumes of data, data scientists also need to have a strong background in statistics and mathematics. Other things that are key for careers in this field include knowledge of business analytics, data analytics, and soft skills like decision-making, research, and communication skills. 

The main difference between a data scientist and similar roles like data analyst is the breadth and complexity of their skill sets. Data scientists have skills that span multiple disciplines, combining strong knowledge of the business world and best practices with expertise in information technology. This gives them the flexibility to deliver meaningful insights, not just based on the numbers and facts, but on their understanding of the broader business landscape,

Individuals who thrive in data analysis are those with an inquisitive mind and an ability to solve complex problems through logic, information, and research. They tend to enter the field with at least a Bachelor’s degree in a field like computer science, information technology, applied mathematics, or statistics. Once a professional has this training, there are a wide range of different roles they can take, depending on their specific interests and skills, up to executive leadership roles like Chief Data Officer or Chief Analytics Officer.  

Job opportunities in data science

As you saw from the statistics in the introduction, companies today have a growing need for professionals with expertise in data analytics. Data from Deloitte indicates there will be a shortage of roughly 250,000 data scientists by 2024, and while new technologies like low-code platforms and AI can automate some of these tasks, businesses across fields still have a growing need for the insights and value a data scientist provides. 

One reason there is so much demand for data scientists is that they can fill a variety of positions. Here are some of the most common roles for these professionals that you’re likely to see on the U.S. job market today. 

Data engineer

Data engineers are the data professionals who build and test big data ecosystems for organizations. They are often also responsible for updating and improving existing systems and databases to improve their efficiency and functionality. The main distinctive feature of this role is that engineers are focused on hardware and systems rather than specifically analysis of data. 

Key skills of data engineers:

  • Experience with programming languages like R, Ruby, Java, and C++
  • Experience working with ETL tools and data APIs
  • Background in data engineering

Common responsibilities of data engineers:

  • Build and maintain databases and data management systems
  • Collect and manage data
  • Analyze data to find patterns and forecast trends
  • Report to leadership and stakeholders based on findings

Data architect

Like data engineers, architects are on the planning and system side of data analysis. They plan the strategy and management of databases to ensure they’re secure, centralized, and easily integrated into other business systems. This tends to be a more senior role, with professionals working for a few years as a data analyst before taking this type of position.

Key skills of data architects:

  • Experience with programming languages like SQL, XML, and Spark
  • Experience with data warehousing, data modeling, and ETL
  • Experience with cloud computing design, architectures, and data lakes
  • Expertise in Hadoop technologies such as Hive, Pig, or MapReduce

Common responsibilities of data architects:

  • Develop and implement the overall data strategy of the organization
  • Plan and manage the end-to-end data architecture
  • Maintain and optimize the security and efficiency of databases
  • Audit data management system performance to identify areas for improvement

Database administrator

A database administrator oversees the storage of a company’s data and controls who has access to it. They work closely with cybersecurity teams and need a firm command of information security concepts like access control and data recovery. This role may also be responsible for developing databases in smaller companies.

Key skills of database administrators:

  • Knowledge of database management systems and query languages like SQL
  • Experience with data backup and recovery
  • Knowledge of data standardization policies and practices

Common responsibilities of database administrators:

  • Install, configure, and maintain database management systems and software
  • Monitor the performance of and access to databases
  • Perform data archiving, backup, and recovery
  • Develop and implement standards for data management

Statistician

Where the above roles are concerned with the systems and processes surrounding data, statisticians focus on deriving insights from the data itself. They use mathematical concepts and techniques to interpret data sets and use that knowledge to solve problems in the real world. This is a skill needed in a range of industries, including insurance, healthcare, marketing, engineering, and government.

Key skills of statisticians:

  • Strong logical and mathematical reasoning skills
  • Experience with data mining and machine learning technologies
  • Experience with a variety of database systems

Common responsibilities of statisticians:

  • Gather relevant data and design data collection systems and processes
  • Analyze data to predict trends and relationships using statistical methodologies
  • Prepare reports on findings and communicate them to coworkers and leadership

Business analyst

This role differs slightly from other jobs in data science because it’s more focused on deriving actionable business insights than on the data itself. That said, they still work with large volumes of data, and need to have the same in-depth understanding of data modeling, visualization, and analysis techniques. They often serve as the go-between for the IT and decision-making areas of an organization. 

Key skills of business analysts:

  • Experience with data modeling and data visualization tools
  • Thorough understanding of business practices and principles
  • Strong knowledge of math and statistics

Common responsibilities of business analysts:

  • Apply data insights to improve existing business processes
  • Develop and implement new technology and systems
  • Perform detailed analysis of business problems, opportunities, solutions, and forecasting

Machine learning engineer

This is among the most in-demand roles in data science today, as more organizations adopt AI and other automation technologies. Machine learning engineers use advanced math and statistics concepts to work with big data, allowing businesses to gain new understandings of their customers, competitors, and broader industry. Several years of experience in a software engineering or data science role is typically a prerequisite for these positions. 

Key skills of machine learning engineers:

  • Experience building data pipelines and performing A/B testing
  • Experience with machine learning libraries and frameworks
  • Thorough knowledge of SQL, REST APIs, and machine learning algorithms

Common responsibilities of machine learning engineers:

  • Design, train, and test machine learning systems and algorithms
  • Develop software, applications, and other products utilizing machine learning systems
  • Model and visualize data to derive meaningful insights

Typical salary and compensation for data scientists

Salaries across data science roles tend to be well above the national average. In part, this is because these services are in such high-demand, and tend to be critical to the overall performance of businesses. Because of this, employers are willing to pay a premium to hire these important employees who will help them make better decisions. 

Entry-level roles in data science include titles like Junior Data Analyst or Junior Data Scientist. Even at this early career stage, the average salary on Glassdoor starts at a base pay of $58,000 a year. Mid-career data scientists can earn anywhere between $81,000 and $148,000 per year, with an average salary of around $111,000.

More specialized and advanced roles within data science earn similarly high compensation. The current median salary rates for the roles described above in 2023 are:

Advantages of pursuing a career in data science

The combination of high pay and ample job opportunities makes data science a very enticing career for those who enjoy math, numbers, and statistics. There are a number of other factors that make this career path very appealing, as well, such as:

  • Versatility and range of work. Data science is a great career path if you like solving new problems and facing new challenges in your day-to-day work. The wide range of career paths and job options in this field give professionals a lot of variety when it comes to choosing the type of work they do. 
  • High job satisfaction. In a survey of current data scientists, 81% of respondents ranked their workplace happiness as a 4-5 on a 5-point scale. If you like doing interesting work that provides real value to your company, this is a role that will bring you a lot of satisfaction. 
  • Flexibility. Spiceworks listed data scientist as one of its top 5 remote tech jobs, with roughly 30% of job opportunities offered remotely. Even when you’re expected to be in the office, a data scientist’s work is largely independent, and these professionals enjoy a high level of workplace flexibility compared to other fields. 

Challenges and risks of the data science field

Of course, no career path is perfect. Along with the many benefits of working as a data scientist, there are some challenges you’ll face in this field. One of the biggest challenges that these professionals face is the fact that data science is constantly evolving. Keeping your skills current requires continuous learning and new skill development. There are also so many different aspects of data science that most professionals are never able to master the full breadth of the field. 

Many areas of data science also have a fairly steep learning curve, requiring professionals to learn complex concepts and highly-specialized topics on an ongoing basis. This can be exciting for people who like a challenge, but it can also be overwhelming. The specific roles within data science can also often be blurred, especially in teams that are struggling to hire enough data scientists for their business needs. Some find the lack of clearly defined expectations and responsibilities to be the most stressful aspect of this job. 

That learning curve can also give data science a high barrier to entry. A Bachelor’s degree is usually just the minimum requirement, and many professionals in this field also have a Master’s degree, as well as several independent professional certifications. Complicating matters further, many entry-level roles for data scientists are prime candidates for automation and AI. Considering that many employers look for several years of experience when hiring candidates for more advanced roles, this can make it a challenging field to break into. 

So is data science a good career?

Data science offers numerous desirable career paths for the right kind of professional. If you love math, have a background in computer science, and want a job that will offer new challenges and learning opportunities on a regular basis, then data science can be an ideal fit.