Data Scientist Job Outlook 2025

Have you heard the saying "Knowledge is power"? Well, this couldn't be more true in 2025— especially when looking at the data scientist job outlook.

According to Statista, around 132 zettabytes of data were produced worldwide in 2023 alone. That's 132,000,000,000 terabytes—a lot of data. We're in the middle of a data gold rush, and businesses are taking advantage

The Harvard Business Review declared over 10 years ago that data science is the sexiest job of the 21st century. But given the recent tech layoffs, you might wonder if the current data science job outlook is still as bright.

There are still about 21,000 new data science job openings projected each year over the next decade. The key to success? Adapting to the market's changing demands and developing the specific skills employers are looking for today.

This article is our latest annual analysis of the data scientist job market. We've examined over 1,000 job postings to bring you current insights into what employers want from data scientists in 2025.

Key Insights

• Machine learning is becoming more complex and specialized
• Data engineering skills are growing in demand alongside AI and ML expertise
• Only 5% of jobs are fully remote
• Most data science job postings (57%) seek "Versatile Professionals"—candidates with expertise across multiple domains rather than deep specialization
• 38% of roles focus on "Domain Experts" with specialized skills in areas like Machine Learning 
• Only 5% require "Full-Stack Data Scientists" who can handle the end-to-end data lifecycle
• Salary trends show a high-value market: The majority (32%) of data science jobs in 2025 offer salaries between $160,000 - $200,000, with $120,000 - $160,000 following closely at 27%
• Machine learning remains essential for AI roles—77% of AI-related job postings require machine learning skills

Table of Contents

Methodology

What do Data Scientists Do?

How Can You Become a Data Scientist?

What Technical Skills Do You Need as a Data Scientist?

How Can You Land a Data Scientist Job?

What is the Data Scientist Job Outlook in 2025?

Become a Data Scientist with 365 Data Science

FAQs

Methodology

We analyzed 1,121 job postings from Glassdoor US to help you get a better understanding of the data science job market in 2025.

After cleaning and removing duplicates, we extracted key data science keywords and requirements from each listing. This comprehensive analysis reveals current job responsibilities, skill requirements, and industry trends.

To make our findings clear and comparable, we've presented all data as percentages and created visual representations that highlight the most important trends.

Note that last year's research used job postings from Indeed US. This difference in job board platforms should be considered when comparing results.

What do Data Scientists Do?

Last year, our data revealed a clear trend across data-related roles. Job listings weren’t just looking for specialists to work with one aspect of the data lifecycle, they sought professionals who could work with data end-to-end, combining skills from multiple roles.

This year, we took this into account, with our new Job Posting Specialization Score (JPSS). But before we get into it, let’s look at the traditional data scientist job description.

Data Scientist Job Description

Understanding data scientist requirements is crucial for those interested in this field.

Data scientists are analytical experts who bridge the gap between business challenges and data-driven solutions. They design and implement machine learning models, develop AI-driven applications, and transform raw data into business insights.

The more data becomes available, the more businesses need help making sense of it—driving continued strong demand for data scientists through 2025. Working closely with stakeholders, they identify opportunities for data-driven innovation and automation.

The role typically requires:

• Advanced machine learning and AI implementation expertise
• Business strategy and problem-solving capabilities
• Experience with large-scale data processing and analytics
• Strong stakeholder communication skills

Job Posting Specialization Score (JPSS)

This description holds true for specialized data science roles—but does this reflect the actual state of the data science job market? That’s where the JPSS comes in!

The JPSS is a metric that measures whether job postings in data science seek highly specialized professionals or candidates with a broad range of skills. Through job description analysis, we identified if employers want domain experts, versatile professionals, or full-stack data scientists.

We analyzed job postings using 11 specialization categories. Each job received a JPSS score reflecting how many distinct skill categories were required to give you a better understanding 2025’s data scientist job outlook. Here’s how we classified them:

Here are the results:

Consistent with 2024's findings, the majority (57%) of job postings seek highly versatile professionals.

To land a job, your best bet is to develop a wide range of skills—from data analysis and database management to advanced ML. While only 5% of positions require managing the entire data lifecycle, the data scientist job outlook indicates that developing broad expertise seems to be the safest path to securing a role this year.

The key to building a broad skillset is continuous learning. Check out our learning platform, 365 Data Science, to explore courses covering every topic you'll need for data and AI roles—from programming with Python and SQL to advanced AI applications.

That said, 38% of the postings look for Domain Experts—meaning if you can differentiate yourself from competitors in a specialized domain, there are still many opportunities available for you.

How Can You Become a Data Scientist?

Before we take a closer look at these specific skills, let’s explore what kind of educational background employers are looking for. If you’re wondering “How to become a data scientist,” here’s what you need to know.

Data Scientist Education Requirements

Exploring the data scientist education requirements this year shows a trend towards higher education in the field. Here’s the breakdown.

The path to becoming a data scientist typically starts with a bachelor's degree in a quantitative field like computer science, statistics, or mathematics.

An interesting development in this year’s data scientist job outlook is the growing requirement for a data science-specific degree.

While this field barely existed in universities a decade ago, it has become more and more prevalent. Job postings requiring a data science degree have jumped dramatically—from 47% in 2024 to 70% in 2025, marking a 23% increase.

This isn’t to say you can’t find a job with a different degree—related fields like statistics and computer science are mentioned in over 50% of the job postings, with engineering, math, and machine learning following close behind.

Similar to last year, several seemingly unrelated fields appeared on our list, including economics, architecture, physics, and chemistry. These fields may indicate domain-specific roles or reflect shared core competencies like analytical thinking and attention to detail.

Now that you know what kind of degree you’ll need, let’s see what level of education is ideal for data scientist roles in 2025.

While some professionals enter the field with just a bachelor's degree and relevant certifications, many pursue master's degrees to advance their careers. In fact, the proportion of data scientist jobs looking for graduate level education has grown since last year.

As you can see in the table:

• The percentage of job posting mentioning PhDs jumped up by over 10%.
• The percentage requiring bachelor’s degrees decreased by over 3%.
• The proportion of jobs not mentioning any specific degree decreased by almost 10%.

What does this mean for you? It appears that employers see bachelor's degrees as insufficient for developing all the skills needed for data science roles, leading them to favor candidates with higher degrees.

If you're looking to break into a data science career in 2025, the data scientist job outlook shows that pursuing a graduate-level program could be beneficial. But the main drawback, of course, is the time investment—you'll need to dedicate at least two more years to your education.

Fortunately, there are many alternatives to formal education. Industry certifications in specific tools or platforms can also help demonstrate expertise to potential employers.

The market now offers numerous certification platforms that provide credentials for specific skills or complete career tracks.

You can enhance your bachelor's degree with targeted certifications from our learning platform 365 Data Science—we offer all the topics you need to secure a job this year!

What Technical Skills Do You Need as a Data Scientist?

Experience shows that employers consistently prioritize practical skills over degrees—especially when candidates can effectively demonstrate their expertise. Let's examine the specific data science skills employers are seeking in 2025.

Programming Skills for Data Science

The first skills you’ll often find in a data scientist career track is programming. This is because it's the foundational tool that enables all other data science work, from data processing and analysis to building complex machine learning models.

To excel in data scientist jobs, proficiency in programming languages like Python and SQL is essential.

In traditional statistics and data analysis, programming was viewed as an extra bonus skill, and was not a core requirement. Our research over the years, however, has shown that programming skills are slowly moving down the ladder, from tech-centric ML engineer roles down to data analysts.

This is reflected in our research into the 2025 data science job outlook. In 2024, Python was the most popular programming language for data scientists, followed by R. Python offers versatility across all tasks, while R specializes in statistical analysis. SQL was the third most requested language.

In 2025, we've seen increased mentions of programming skills across the board—highlighting their growing importance. Notably, SQL has moved ahead of R to become the second most required programming language.

This shift reflects the increasing focus on data infrastructure and the need for data scientists to work directly with databases, as organizations prioritize building robust data pipelines and maintaining data quality at scale.

Across all roles, we found SQL to be our most in-demand skill for 2024. So far, it continues to hold this position in 2025’s data scientist career outlook.

Looking to master this year's most sought-after skill? Our comprehensive SQL course provides 11 hours of lessons with hands-on exercises, taking you from complete beginner to advanced database management.

Data Analysis Skills

The next step in your learning journey will likely be data analysis. It's the foundation that enables data scientists to understand patterns, ensure data quality, and build effective machine learning models.

Without strong analytical skills, even advanced AI applications would be ineffective, since they rely on properly understood and prepared data.

Our data shows that employers increasingly recognize the importance of a strong foundation. The top three data analysis skills—data visualization, statistical analysis, and data mining—remain unchanged since 2024, but they have become even more crucial.

Data visualization has seen a dramatic increase from 10% to 22%, while statistical analysis has risen from 8% to 18%.

Let’s take a closer look at the top data analysis skill for data scientists—data visualization.

Data Visualization Tools

Matching the doubled mentions of data visualization in this year's job postings, specific visualization tools have also seen their importance double.

Tableau and Power BI are dominating in this year’s data scientist job outlook. They’re the most widely used data visualization tools in data science, as they offer intuitive interfaces, features for creating interactive dashboards, and seamless integration with various data sources.

For data scientists, data visualization tools help non-technical stakeholders make connections between complex data analysis and the stories they tell.

Want to tell stories with your data? Check out our introductory Tableau and PowerBI courses to turn your analysis into understanding.

Microsoft Office Tools

Many companies assume all applicants will have a basic understanding of Microsoft Office tools, which explains why these skills are mentioned less frequently compared to other skills in this article.

Nevertheless, knowledge of Word and PowerPoint are essential for reporting and presenting findings, and should be mentioned in any list of data science skills.

An even more integral tool in all steps of the data lifecycle is Microsoft Excel. Its versatility in data manipulation, basic analysis, and quick calculations makes it essential even for advanced data scientists who primarily work with programming languages.

If you've never used Excel before, our Intro to Excel course should be your first step into the field.

Machine Learning and AI Skills

What sets data science apart from data analytics is the incorporation of machine learning methods. 

In simple terms, while data analytics tells you "what happened and what might happen next based on past patterns," data science can tell you "why it happened and what might happen in complex scenarios we've never seen before." This is thanks to machine learning, the most important set of skills in the 2025 data scientist job market.

Machine learning (ML) is a subset of AI that lets computers learn from data and improve their performance on tasks without explicit programming for each scenario.

Data science job growth is particularly strong in areas requiring expertise in machine learning and AI, reflecting the increasing adoption of these technologies in various sectors.

This is reflected in the data. More than three quarters (77%) of job postings require ML skills.

While there are a few other AI skills to keep in mind to stay competitive—like NLP (18%) and MLOps (8%)—none are as crucial to data science positions in 2025 as machine learning.

Let’s take a closer look.

Machine Learning Skills for Data Science

Looking at the 2025 data, deep learning and artificial neural networks have emerged as highly sought-after data science skills.

While NLP has seen only modest growth since 2024 (around 3% increase), mentions of deep learning have doubled, now appearing in around 20% of data science job postings.

This surge in demand reflects the growing adoption of AI technologies across industries—making a general understanding of these methods highly beneficial for those entering the job market this year.

While some job postings mention more specific techniques like reinforcement learning, unsupervised learning, and even specific algorithms like linear regression, most employers who list ML skills likely expect these to be part of that package.

Looking for a crash course in machine learning, we have a trio of courses led by top Netflix data scientist Jeff Li and amazing content creator Ken Jee! Start your ML journey today!

ML and AI Tools for Data Science

Deep learning's importance has doubled since 2024, and along with it, the demand for TensorFlow (23%) and PyTorch (21%)—the primary frameworks used to build and deploy deep learning models.

This shift enables businesses to automate increasingly complex tasks, from customer service chatbots to predictive maintenance systems, resulting in substantial cost savings and operational efficiency.

Scikit-learn has equally jumped from 6% to 15% since 2024, reflecting the growing need for traditional machine learning algorithms and data preprocessing capabilities in production environments.

Interdisciplinary Technical Skills

In this section of the data scientist job outlook for 2025, let's jump back to the topic of job specialization.

Our analysis found that 57% of the data scientist job postings look for versatile professionals who can handle more aspects of the data lifecycle than core data science abilities.

We also noted above that SQL has risen in importance dramatically over the last few years. This ties in with the fact that data engineers have become one of the most sought-after data-related roles. Employers, however, don’t always separate data engineers from scientists, despite key differences between the roles.

Data engineers build and maintain data infrastructure and pipelines, while data scientists analyze that data and build models to extract business insights. In other words, they work with two very different parts of the data lifecycle.

But, as we have seen, employers seeking data scientist who can tackle both roles have become the norm in the data science job market.

According to the table, big data and data pipeline skills appear in more than 10% of data science job listings for 2025. This is particularly relevant for smaller organizations, where data scientists are often expected to handle the entire data lifecycle—from collection and storage through to analysis and modeling.

Once again, we see a large jump from 2024, where big data and data pipelines were only mentioned in about 8% and 5% of job postings respectively.

This is even more obvious when looking at the required data engineering tools. We have already discussed SQL’s importance in data science, but both Apache Spark and Microsoft Azure have made their way into over 15% of data scientist job postings.

Apache Spark is a distributed computing system for processing large datasets across multiple computers simultaneously, making it essential for big data analysis. Microsoft Azure is a cloud platform providing data storage, computing power, and analytics tools.

Both data engineers and data scientists need these tools when working with massive datasets that exceed the capabilities of traditional tools like Excel or local Python installations.

Looking to learn data engineering quickly? We've got you covered! Our Intro to Data Engineering course and Building Data Pipelines with Apache Airflow course will help you become more competitive in the 2025 data science job market.

Although less prevalent than data engineering, data architecture skills have also found their way into data scientist job postings this year—particularly cloud computing.

Cloud computing allows users to access computing power, storage, and software over the internet rather than on local computers.

Let’s take a closer look at some specific cloud technologies.

Cloud Skills for Data Science

As data science demand continues to rise, skills in cloud technologies like AWS and Azure are becoming increasingly valuable.

AWS and Azure are the leading cloud platforms in data science, appearing in 27% and 16% of job postings respectively. They offer essential tools for data storage, processing, and machine learning.

AWS provides services like S3 for storage and SageMaker for ML model development, while Azure offers Databricks for analytics and Azure Machine Learning for model deployment.

Both platforms are crucial for handling large-scale data operations, making cloud skills important in data science roles. This trend has remained consistent since last year, with similar percentages found in our 2024 data.

Some of our latest courses cover hands-on cloud use-cases with tools like AWS, Azure, and Google Cloud Platform. Check out our courses: Building Data Pipelines with Apache Airflow and Introduction to Data Architecture.

How Can You Land a Data Scientist Job?

The next section of our data scientist job outlook research examines two critical aspects: location and salaries.

Where Do Data Scientists Work?

Our 2025 job posting analysis revealed a significant shift: for the first time, New York—not California—claimed the top spot for data scientist positions!

The data shows a clear concentration of opportunities in these two states, with both appearing in more than 15% of job postings. The remaining top 10 states—New Jersey, Virginia, Washington, Texas, Illinois, and Massachusetts—each account for just 2% to 7% of listings.

This shift from California to New York likely stems from NYC's strong financial sector, diverse industries (healthcare, media, retail), and growing fintech scene. Additionally, recent tech industry changes in Silicon Valley may have temporarily reduced California opportunities.

But let’s talk about the question that’s probably only everyone’s mind.

Remote Work for Data Scientists

The proportion of job postings explicitly mentioning remote positions remains unchanged since 2024.

Remote data science opportunities comprise only 5% of data scientist job postings. While this percentage seems low, several factors provide important context.

First, 31% of job postings don't specify a location at all. These companies may offer remote work without explicitly stating it in their advertisements.

Second, hybrid work has become increasingly common. Statista reports that approximately 50% of US workers in 2024 had hybrid roles—combining both office and remote work options.

Work norms have evolved significantly since the remote work boom of 2020–2021. With more employees expecting flexible work arrangements, most companies have adopted a hybrid approach.

This trend is particularly relevant if you're considering working for a company outside your home country. Relocation may have to be an option for you to work in those coveted roles.

How Much Do Data Scientists Make in 2025?

According to Glassdoor, the average data scientist salary in the US for the first quarter of 2025 is around $166,000. Here’s the breakdown by years of experience:

0-1 Year: $152,000

2-3 Years: $167,000

4-6 Years: $181,000

7-9 Years: $193,000

10+ Years: $215,000+

This is a huge jump from early 2024, when the average entry-level data scientist salary was $117,000—a leap of nearly $40,000. This substantial rise shows that companies are eager to pay premium wages for data and AI expertise, suggesting a thriving job market.

Our own market research corroborates this.

The most frequently mentioned salary range in our data was $160K-$200K, suggesting that a career in this field offers strong earning potential.

How Many Years of Experience do Data Scientists Need?

The data science career outlook is influenced significantly by the amount of experience candidates bring to their roles, affecting job availability and salary prospects

We noticed an interesting shift in the required levels of experience for 2025 since 2024.

While demand for mid-level professionals (2-6 years) remains highest, the market has shifted toward more experienced candidates. In 2024, positions requiring 6-8 years of experience were least common, followed by 8+ years and then 0-2 years. By 2025, this pattern has reversed: entry-level positions (0-2 years) are now least common, followed by 6-8 years and 8+ years of experience.

This reflects a shift in the data science job market, with employers now favoring experienced professionals over newcomers.

Like the degree requirements mentioned earlier, this trend suggests the industry is maturing and placing greater value on proven expertise.

What is the Data Scientist Job Outlook in 2025?

So, is data science a good career in 2025? The data points to yes!

The data scientist job outlook for 2025 remains robust and promising, with several key trends shaping the landscape:

• Salaries have seen a remarkable increase, with entry-level positions now averaging $152,000—up by $40,000 from 2024
• New York has overtaken California as the top location for data science positions, reflecting a geographical shift in opportunities
• Machine learning skills are in extremely high demand, appearing in 77% of job postings
• The industry is maturing, with a growing preference for experienced professionals and increased emphasis on interdisciplinary skills

These trends and statistics support a positive data science career outlook for the coming years, making it a promising field for new entrants.

While the job market has evolved to favor experienced candidates and demand more comprehensive skill sets, the substantial salary increases and diverse geographical opportunities suggest that data science remains a highly valuable and rewarding career path.

So, is data science oversaturated? Not if you have the right skills.

Become a Data Scientist with 365 Data Science

At 365 Data Science, we offer a comprehensive Data Scientist Career Track designed to take you from beginner to job-ready professional. Our carefully structured curriculum covers all the essential skills highlighted in this job outlook report—from Python and SQL fundamentals to advanced machine learning and deep learning with TensorFlow.

We design our curriculum to meet the data science job requirements of today and tomorrow, ensuring our courses prepare you thoroughly for the job market.

The Career Track includes hands-on projects with real-world datasets. Plus, our courses are constantly updated to reflect the latest industry trends and technological advances.

Ready to start your journey toward a rewarding career in data science? Sign up today and join thousands of students who have successfully transitioned into data science roles. Your future in data science starts here!

We hope this data scientist job outlook as helped you decipher what skills you need to develop to land your dream role in 2025!

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