6 In-Demand Data Scientist Jobs in 2023

The data science industry is growing at a rapid pace. Technology, big data, and software are advancing every day and there are plenty of jobs to reflect this demand. According to US News and World Report in 2022, information security analyst, software developer, data scientist, and statistician ranked among the top 10 (out of 100) jobs [1].

With more access to data science courses and certificates, anyone can get a job in data science in 2023. Here is a guide to the most in-demand jobs in data science.

What is data science?

Data science is 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: What Is Data Science? Definition, Examples, Jobs, and More

Are data scientist jobs in high demand?

Data scientist jobs are predicted to experience 36 percent growth between 2021 and 2031, according to the US Bureau of Labor Statistics [2]. Operations research analyst (or data analyst) jobs are projected to grow 23 percent [3].

Due to the high demand and technical skill set, jobs in data science tend to pay well. Data science is penetrating nearly every industry, from health care to retail to technology, so there are plenty of options to find your niche. If you're interested in this field, you might consider studying statistics, mathematics, programming, coding, and software development.

Read more: How to Become a Data Scientist

6 in-demand data science jobs

Data science jobs are becoming commonplace and necessary for companies across the globe to optimize quality and financial growth. Let’s dive into some of these in-demand roles.

1. Data scientist

Median annual salary: $103,140 [4]

Data scientists go through and determine the questions their team should be asking. They figure out how to answer those questions with data, often developing predictive models and algorithms to theorize and forecast outcomes.

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

2. Data analyst

Median annual salary: $67,150 [5]

A data analyst collects, analyzes, evaluates, reviews, and organizes data. They'll organize the data and perform statistical calculations in such a way that they can find trends that can solve problems for a client or for their employer and inform important business decisions.

Read more: What Does a Data Analyst Do? Your 2023 Career Guide

3. Data engineer

Median annual salary: $94,067 [6]

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 make the data easy to digest so it can be processed and used to help a company or customer. 

Read more: What Is a Data Engineer?: A Guide to This In-Demand Career

4. Data architect

Median annual salary: $119,156 [7]

Data architects create plans for systems to manage and organize data. An architect will consider a company’s plan for solving a particular issue and construct a system that digests information and presents it so data scientists can work with the trends and patterns.

Read more: What Does a Data Architect Do? A Career Guide

5. Machine learning engineer 

Median annual salary: $108,402 [8]

Machine learning engineers are specialists that design the architecture for artificial intelligence (AI) 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, filter data, and perform algorithmic calculations. Machine learning engineers specialize in programming applications that stand alone and automate processes with artificial intelligence. 

Read more: What Is a Machine Learning Engineer? (+ How to Get Started)

6. Business intelligence engineer

Median annual salary: $94,607 [9]

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.

How do I prepare for a career in data science?

To get started, you may want to research the many options of online courses, bootcamps, workshops, and certifications out there. There is no set path to becoming a data scientist. However, you can find courses and degree programs from leading universities on Coursera and refer to this archive of Coursera's data science articles, Data Science Jobs Guide: Resources for a Career in Tech.

Study hard

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

University 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 related skills and systems. Data science certifications can demonstrate 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 in the job description, 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)

Gain experience

There are several ways to get your foot in the door and start gaining data science experience. 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. 

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

Finally, hackathons 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 that a data science professional would be doing.

Start your data science career today

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

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

(61,871 ratings)

172,612 already enrolled

BEGINNER level

Learn More

Average time: 5 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

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

(105,592 ratings)

1,492,770 already enrolled

BEGINNER level

Learn More

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