The Most In-Demand Data Scientist Jobs for 2023

Written by Coursera • Updated on

Data science jobs are becoming increasingly in demand as big data and technology industries grow. Find out which jobs are the hottest and how to prepare for your career.

[Featured Image]: Data architect creating a plan to organize data for the company.

The data science industry is growing and changing at a rapid pace. With technology, big data, and software advancing, the value of data and employees who can work with data is increasing. Students have greater access to data science courses and resources now more than ever, and many tools are available to anyone interested in pursuing a career following graduation. Data science students are being recruited and hired in higher numbers due to the demand for employees who speak the language of data. 

What is data science?

Data science consists of the preparation, management, analysis, organization, and mathematical processing of data used to develop solutions to challenges a company or individual faces. Data scientists utilize analytics, statistics, and software to manage massive amounts of data and will work at different stages of digesting these large data sets in collaboration with other professionals to solve a problem.

Read more: How to Become a Data Scientist

Are data scientists in high demand?

The US Bureau of Labor Statistics predicts that mathematician and statistician roles, including data scientist jobs, will experience 36 percent growth between 2021 and 2031, which is much faster than the average 8 percent for all occupations [1]. This equates to an annual increase of approximately 48,800 job opportunities. 

Jobs in data science typically have an average base salary of $100,910 [2]. Statistics, Mathematics, Programming, and Project development are all academic fields involved in data science and are increasing in value due to the demand for big data for nearly every industry. 

Most in-demand data science roles

Some of the most in-demand data science jobs are data analysts, data engineers, data architects, machine learning engineers, and business intelligence engineers. Data science jobs will soon become commonplace and necessary for companies across the globe to optimize quality and financial growth. Let’s look at some of these roles in more detail:

Data analyst

Median annual salary (Glassdoor): $99,576 [2]

A data analyst collects, analyzes, evaluates, reviews, and organizes data. A data analyst will look to organize the data and perform statistical calculations in such a way that they can find trends in the data as a way to solve a problem for a client or for their employer and inform important business decisions.

Data engineer

Median annual salary (Glassdoor): $94,067 [3]

Data engineers build systems that can automatically collect, store, manage, and analyze chunks of data so that other data scientists and mathematicians can further look at trends and patterns for interpretation. They want to make the analyzed data easy to understand and digest so the data collected can be efficiently processed and used for information that can help a company or customer. 

Data architect

Median annual salary: (Glassdoor) $118,499 [4]

Data architects create plans for systems that manage and organize data. An architect will consider a company’s plan or idea for solving a particular issue and look to construct a system that can digest information and present it understandably so that other data scientists or specialists can take the data and find trends and patterns. They want to make the data accessible to those who need to process it so that companies can critically attack the problems they are using data to solve. 

Machine learning engineer 

Median annual salary: $130,094 [5]

Machine learning engineers are specialists that design the architecture for artificial intelligence programs to interact with large data sets. These engineers typically work with other data scientists and programmers to create artificial intelligence programs that can detect patterns in data, filter data sets for important contents, and perform algorithmic calculations to decipher the meaning of the collected data. Machine learning engineers specialize in programming applications that stand alone and automate processes with artificial intelligence. 

Business intelligence engineer

Median annual salary: $118,016 [6]

Business intelligence engineers design, install, maintain, and develop data systems that analyze large chunks of data specifically for financial and business purposes. They create interfaces that allow for easy access and digestion of data for employees to look at relevant task data. They can also work on other systems, such as databases and dashboards that users interact with, to evaluate data clusters efficiently and comprehensively.

Read more: A Guide to Data Science in Business: Benefits and Uses

How do I prepare for a career in data science?

With online courses, bootcamps, classes, workshops, and certifications, many routes are available to prepare for a career as a data scientist. You can find courses and degree programs from leading universities on Coursera and many other resources to pursue certifications, internships, or employment. 

Get experience

There are several ways to get your foot in the door and start establishing experience to prepare you for jobs in data science. If you are completing a degree in a data science-related subject, some of your classes will be practical projects requiring the completion of hands-on activities and projects. You can use these to build a portfolio you can present to an employer. 

Open source development is another way to get some relevant work experience. Open-source programs allow users access to the source code of their software, and you can apply your programming knowledge to an open-source program and make changes to the software you already use. Applying your skills to people's daily applications is a great way to showcase your knowledge and understanding of the software. 

Hackathons and data science competitions are great ways to test your skills against others in your field and collaborate with a team on a project. These competitions are an excellent introductory experience for someone looking to get into the data science field because they replicate the teamwork, pressure, and type of work, someone in data science, would be doing and look great on your resume. 

Study hard

With a willingness to learn and determination, anyone interested in a career in data science can learn and grow into a valuable asset to a company or team. Pursuing an education in this field can give you the skills and knowledge you need to start. The minimum requirement for a data scientist is a bachelor’s degree in a relevant subject such as IT, computer science, or mathematics. Some employers will require a master’s degree. 

College degrees aren’t the only means of study, and in this field, employers welcome all forms of learning and are impressed by the certifications and courses you have taken. 

Get certified 

A great way to start pursuing a career in data science is to get certified in many related skills and systems. Getting certified in a particular platform or environment will show your expertise and thorough understanding of the systems you may be using. If you have a specific position in mind, you can usually see a  list of the types of systems required, which can help guide you on what certification is best for your career and interests. Some possible certifications include:

  • Certified Analytics Professional (CAP)

  • SAS Certified Big Data Professional 

  • Oracle Business Intelligence (BI)

Get started

Coursera is a great place to start exploring a certificate that is right for you, with many different Professional Certificate courses from top companies and universities in the industry. Consider starting with an IBM Data Science Professional Certificate or a Google Data Analytics Professional Certificate.

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professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

4.6

(59,660 ratings)

140,102 already enrolled

BEGINNER level

Average time: 11 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, CRISP-DM, Data Analysis, Pandas, Numpy, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Dashboards and Charts, dash, Matplotlib, SciPy and scikit-learn, regression, classification, Hierarchical Clustering, Jupyter Notebook, Data Science Methodology, K-Means Clustering

Placeholder

professional certificate

Google Data Analytics

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.

4.8

(92,836 ratings)

1,243,975 already enrolled

BEGINNER level

Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

Article sources

1

U.S. Bureau of Labor Statistics. “Occupational Outlook Handbook: Mathematicians and Statisticians, https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm#tab-8.” Accessed November 14, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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