Data Scientist Salary: Your 2025 Pay Guide

Written by Coursera Staff • Updated on

Learn how your location, education, industry, and experience can play a role in how much you can earn as a data scientist.

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Businesses generate massive amounts of data every day, from customer information to inventory tracking. Rather than leaving this information on the ground, businesses task data scientists with managing and transforming it into actionable insight so that they can make better-informed decisions.

If you enjoy analyzing data to identify patterns and solve problems, this career path offers plenty of well-paid opportunities.

Learn more about what you can expect to earn as a data scientist, including how common factors such as experience and location may impact it.

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Data scientist salary

Data scientists earn a higher-than-average salary. According to the US Bureau of Labor Statistics (BLS), the average annual salary of a data scientist in the US was $108,020 as of 2023 [1].

Other salary aggregation sites, however, note different average and median pay for data scientists. At a glance, explore five different sources say you can expect to earn as a data professional [1,2,3,4,5]:

US BLSGlassdoorIndeedZippiaPayscale
$108,020$118,576$125,633$106,104$101,337

Numerous factors, including your experience level, education, and the location where you work, may influence how much you can expect to earn as a data scientist.

What does a data scientist do?

A data scientist uses their analytical skills to organize and collect data. Data scientists use statistical analysis to report findings, such as trends, within data. As a data scientist, you may suggest strategies or methods to improve operations based on your findings, or you may improve algorithms yourself with a team of others.

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Generative AI, Data Science, Model Selection, Data Analysis, Python Programming, Data Visualization, Predictive Modelling, Numpy, Pandas, Dashboards and Charts, dash, Matplotlib, Cloud Databases, Relational Database Management System (RDBMS), SQL, Jupyter notebooks, Machine Learning, Clustering, regression, classification, SciPy and scikit-learn, CRISP-DM, Methodology, Data Mining, Github, Jupyter Notebook, K-Means Clustering, Data Science Methodology, Rstudio, Big Data, Deep Learning, Quering Databases, Data Generation, Career Development, Interviewing Skills, Job Preparation, Resume Building

Data scientist salary by experience

Typically, more work experience translates to higher pay. The more experienced you are as a data scientist, after all, the more valuable you are to an employer—and they're willing to pay for it. Discover what you can expect to earn by experience level as a data scientist, according to Glassdoor [2]:

  • 0–1 years: $110,755

  • 1–3 years: $119,306

  • 4–6 years: $127,125

  • 7–9 years: $133,377

  • 10–14 years: $145,791

  • 15+ years: $160,545

Data scientist salary by location

The data science field is well-suited for working from home thanks to its emphasis on independent work and technology. The average salary for a data scientist who works from home is $122,738 per year—though remote positions are typically competitive and may generally go to those with the most experience [6]. To help you get a better idea of how your geographic location might impact your salary, explore the average data science pay in different cities across the United States according to Indeed [3]:

CityAverage base salary
Palo Alto, CA$154,987
Herndon, VA$149,214
Seattle, WA$141,216
Boston, MA$125,933
Houston, TX$124,460
Washington, DC$124,059
Denver, CO$120,286
Chicago, IL$112,215
Irvine, CA$111,878

Data scientist salary by industry

The industry you work in can play a significant role in your annual salary. According to Glassdoor, the top paying industries for a data scientist in the US include [2]:

  • Financial services: $131,305

  • Information technology: $149,399

  • Pharmaceutical and biotechnology: $125,151

  • Arts, entertainment, and recreation: $123,389

  • Restaurants and food service: $124,366

Data scientist salary by education

Data scientists must typically possess at least a bachelor's degree to qualify for the job. In fact, according to Zippia, 51 percent of data scientists have a bachelor’s degree, 34 percent have a master’s degree, and 13 percent have a doctorate (PhD) [7]. Data scientists commonly study mathematics, statistics, business, or engineering.

Additionally, Zippia reports the average annual salary for a data scientist with a bachelor’s degree as $101,455 versus that of a data scientist with a master’s degree as $109,454 [4]. This indicates that your data scientist salary will increase as you continue your education.

Job outlook for data scientists

Data science is a fast-growing career. According to the US BLS, the number of jobs for data scientists is projected to grow by 36 percent between 2023 and 2033, resulting in about 20,800 new job openings a year throughout the next decade [1].

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How to increase your data scientist salary

Data scientist salaries typically increase with your education level. If you don't yet have a degree, consider earning a bachelor's or master's degree in data science or a related field. Some higher-paying senior data scientist roles may require a PhD. If you want to obtain an advanced role, consider earning an advanced degree.

Build new skills as a data scientist

Degrees are just one of many ways to increase your salary. You may also obtain a higher salary by expanding your skill set. The list below outlines a few of the most important technical skills for data scientists to master:

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In addition to technical skills and knowledge, it's essential for data scientists to be good communicators. You must be able to report your findings in an easily digestible way so that non-data scientists can understand them. Communication skills are especially important for those pursuing a role in data science management.

It’s really about the necessary skills, and being able to demonstrate that you can do the work. That’s what I achieved by completing this program and earning my credential.

Emma S., on taking the IBM Data Science Professional Certificate

Enhancing your data scientist resume

One way to ensure your most in-demand skills stand out to employers is to earn a Professional Certificate in a relevant area of expertise. Professional Certificates on Coursera's platform are often provided by industry leaders and accredited universities such as IBM, Microsoft, Stanford University, and the University of Colorado Boulder.

Check out this list of online courses that provide you with a data science certificate upon completion:

Read more: How to Create a Striking LinkedIn Profile: Guide + Tips

Getting started as a data scientist on Coursera

Data scientist salaries vary based on factors including the industry you work in, your location, and the highest degree you’ve obtained.

To elevate your chances of becoming a highly paid data scientist, you should focus on advancing your education and gaining hands-on experience. You can begin today by enrolling online to earn an IBM Data Science Professional Certificate.

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Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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696,127 already enrolled

Beginner level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Generative AI, Data Science, Model Selection, Data Analysis, Python Programming, Data Visualization, Predictive Modelling, Numpy, Pandas, Dashboards and Charts, dash, Matplotlib, Cloud Databases, Relational Database Management System (RDBMS), SQL, Jupyter notebooks, Machine Learning, Clustering, regression, classification, SciPy and scikit-learn, CRISP-DM, Methodology, Data Mining, Github, Jupyter Notebook, K-Means Clustering, Data Science Methodology, Rstudio, Big Data, Deep Learning, Quering Databases, Data Generation, Career Development, Interviewing Skills, Job Preparation, Resume Building

Article sources

1

US Bureau of Labor Statistics. "Occupational Outlook Handbook: Data Scientists, https://www.bls.gov/ooh/math/data-scientists.htm." Accessed February 17, 2025.

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