The Data Scientist Job Market in 2024 [Research on 1,000 Job Postings]

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Aleksandra Yosifova 10 Jun 2024 9 min read

After the tech layoffs in 2023 and the emergence of ChatGPT, many thought the golden era of the “sexiest job in the 21st century” was over.

But do these worries reflect the current state of the data scientist job market, or are they simply a short-lived reaction to broader economic challenges?

What skills and competencies does one need today to land a job as a data scientist?

And why do we believe that acquiring data science skills today can be the golden ticket to a lucrative career everyone would be rushing for tomorrow?

We analyzed 1,000 data scientist job postings to reveal what skills employers are looking for and where the market is heading in 2024.

Key Findings

  • 5% of the companies have explicitly set their location to ‘remote’. 
  • 47.4% of the roles require a data science degree.
  • Python was explicitly mentioned in 78% of data scientist job offers in 2023 and in 57% in 2024.
  • Machine learning appears in 69% of job postings.
  • The demand for natural language processing skills has increased from 5% in 2023 to 19% in 2024.
  • Some data science positions require cloud certification, such as AWS (19.7%).
  • The average salary range for a data scientist is \$160,000–\$200,000 annually.

Table of Contents

Data Science and AI: What Does the Future Hold?

Will AI replace data scientists?

Following the mass layoffs in the tech sector and the meteoric rise of platforms like ChatGPT, the question of AI's impact on the data science profession has become critical, challenging the foundation of many to enter or continue in the field. The reality of the situation, however, is much more nuanced and offers a promising outlook for those skilled in data science.

Our comprehensive 2023 tech layoffs study revealed that data scientists constituted merely 3% of those laid off by major tech companies. In comparison, other tech workers—like software engineers—were affected much more, accounting for over 22% of the layoffs. This statistic underscores that data scientists play a crucial role in technological advancements and business, even in the face of mass layoffs.

Does this still hold true after ChatGPT’s disruptive effect on the AI timeline and business landscape?

ChatGPT’s unprecedented popularity led to a burgeoning consensus that AI will not just be a fleeting trend but a dominant force shaping the next decade and beyond. But what does this mean for data scientists?

Far from making the discipline obsolete, the rise of AI underscores the indispensable nature of data science skills. Mastery in statistics, probability, Python, APIs, and machine and deep learning forms the foundational core necessary to understand, develop, and innovate in AI projects.

Data scientists have these skills, and employers recognize it, as evident in the evolving job requirements of 2024.

The expectations toward data scientists are increasing, but these skilled experts are best positioned to excel in an AI-driven era—making them prime candidates for emerging AI-related roles.

So, what skills will a successful data scientist possess in 2024?

Let’s examine our findings to discover.

Research Methodology

We collected 1,000 job postings from Monster Jobs for our comprehensive data scientist job market research. Focused on accurately assessing the data science employment landscape, we removed unrelated positions. This narrowed our dataset to 827 relevant job postings, which served as the foundation of our analysis. Following a meticulous review, we extracted valuable insights about the qualifications and responsibilities associated with data scientist positions.

What Does a Data Scientist Do and How Do You Become One?

The data scientist job description encompasses techniques from statistics to computer science, machine learning, and AI. This role is crucial in translating business objectives into a coherent data strategy and turning raw data into actionable business insights.

Whether the task requires simple data analysis or the development of intricate machine learning models, a data scientist is adept at navigating the complexity of data to find solutions.

While the technical requirements evolve with technological advancements, the fundamental knowledge remains the same. Becoming a data scientist requires a solid foundation in mathematics, statistics, and computer science, complemented by expertise in programming languages like Python or R.

How do you obtain these skills, and can you become a data scientist without a degree?

Let’s examine our results to find out.

Data Scientist Education Requirements

The path to becoming a data scientist entails more than just obtaining a specific degree; it's about acquiring the right mix of skills and knowledge. Despite its increasing popularity, you don’t necessarily need a data science degree to become a data scientist.

Our data scientist job market analysis highlights a range of disciplines valued by employers—showcasing the field’s interdisciplinary nature. The most commonly sought-after backgrounds include:

  • Data science: 47.4%
  • Engineering: 22.6%
  • Mathematics: 21.8%
  • Computer science: 18%
  • Statistics: 17.2%
  • Data engineering: 8.1%
  • Machine learning: 7.9%
  • Chemistry: 5.3%
  • Architecture: 3.6%
  • Artificial Intelligence: 2.9%
  • Physics: 1.6%
  • Economics: 0.7%
  • Information systems: 0.7%

A bar graph representing the percentage of data scientist job postings requiring the following degrees: data science in 47.4%, engineering in 22.6%, mathematics in 21.8%, computer science in 18%, statistics in 17.2%, data engineering in 8.1%, machine learning in 7.9%, etc.

Our research reveals that 20% of employers seek data scientists with a bachelor’s degree, 30% with a master’s degree, and 24% with a PhD.

Another 26% of job postings don’t specify the requirements—indicating that those with no formal qualifications also stand a chance in the application process. Of course, they must demonstrate a strong technical skillset and an outstanding project portfolio to beat the competition.

The 365 Data Science program provides an excellent way to acquire the skillset needed to land a job as a data scientist. We’ve partnered with world-class data scientists from companies like Meta, Netflix, Spotify, and Google to develop a comprehensive curriculum that teaches you business, technical, and soft skills to excel in the ever-evolving work landscape.

A donut chart representing the percentage of data scientist job postings requiring the following education level: PhD in 24.1%, master’s in 29.6%, bachelor's in 19.8%, and not mentioned in 26.5%.

Sought-After Technical Skills for Data Scientists

The variety of technical skills mentioned in job postings reflects the diverse and extensive expertise a data scientist must bring—from basic data analysis skills to advanced specialization in various domains.

Compared to previous years, we see an increase in the demand for advanced data science skills. Still, despite the evolving tech landscape, the core of entering the field remains the same: statistics knowledge and solid data analysis skills form the foundation of data science expertise.

Data Analysis Skills

Employers’ requirements vary from generalized skills like data visualization (10.2%) and statistical analysis (7.7%) to concrete techniques [A/B testing (1.6%), time series analysis (1.3%), etc.] and tools [Statistical Analysis System (SAS), 6.2%].

A bar graph representing the percentage of data scientist job postings requiring the following data analysis skills: 10.2% data visualization, 9.6% data mining, 7.7% statistical analysis, 6.2% SAS, 3.7% predictive modeling, 2.9% exploratory data analysis, etc.

Excel is still an essential asset in the data scientist’s toolbox, mentioned in 10% of job postings—although to a lesser extent than in previous years (26% in 2023). And surprisingly, many offers mention other Microsoft Office tools, too.

A bubble chart representing the percentage of data scientist job postings requiring the following Microsoft Office tools: 10.1% Excel, 3.5% Microsoft Office, 3.5% PowerPoint, 1.3% Outlook, and 0.3% Microsoft Access.

The number of data visualization libraries and software mentioned in data scientist job postings further emphasizes the importance of presenting and communicating the extracted insights. Unsurprisingly, the most popular choices are Tableau (11.5%) and Power BI (9.1%).

A bar graph representing the percentage of data scientist job postings requiring the following data visualization libraries and software: 11.5% Tableau, 9.1% Power BI, 2.7% Matplotlib, etc.

Coding Skills

In addition to data analysis and visualization, coding is the other building block of data science expertise.

And what is the number one programming language for data scientists?

No big surprises here: Python holds the lead with 57%, followed by R (33%) and SQL (30%). Java (9%) and SAS (5%) take the very distant fourth and fifth places, respectively. 

A bar graph representing the percentage of data scientist job postings requiring the following programming languages: 56.7% Python, 33% R, 30.4% SQL, 9.3% Java, 5.1% SAS, 4.7% JavaScript, 4.6% Hadoop, 3.7% Scala, etc.

But while there’s hardly any change in the types of programming languages, the percentage of job offers mentioning them has decreased. In 2023, 78% of job postings required Python, while SQL stood at 60%.

Could this be the impact of AI on the data science job market?

Data Science and AI

Our findings demonstrate that the field’s transformation is already at full speed.

Almost a quarter of job postings contained ‘AI’ or ‘artificial intelligence.’ In addition, we observe a noticeable spike in companies seeking various skills related to developing AI models. Most notably, the demand for natural language processing skills has increased from 5% in 2023 to 19% in 2024.

A bar graph representing the percentage of data scientist job postings requiring the following AI skills: 69.3% machine learning, 21.2% AI, 19% natural language processing, 11.7% deep learning, 3.5% APIs, etc.

Machine learning was mentioned in over 69% of data scientist job postings, with some ads specifying the required algorithms and methods.

A bar graph representing the percentage of data scientist job postings requiring the following machine learning algorithms and methods: 19& NLP, 11.7% deep learning, 9.9% optimization algorithms, 6.2% computer vision, 6% clustering, 5.9% artificial neural networks, 3% predictive modeling, etc.

Other companies went a step further, seeking candidates skilled in AI tools like:

  • PyTorch: 10.8%
  • TensorFlow: 10.4%
  • scikit-learn: 6.4%
  • Keras: 3.6%
  • GPT/ChatGPT: 1.5%
  • Transformers: 1.1%
  • Hugging Face: 0.7%
  • LangChain: 0.4%
  • Theano: 0.2%
  • Pinecone: 0.1%

A bar graph representing the percentage of data scientist job postings requiring the following AI tools: 10.8% PyTorch, 10.4% TensorFlow, 6.4% scikit-learn, 3.6% Keras, 1.5% GPT/ChatGPT, 1.1% transformers, etc.

Although outside the primary requirements, specializing in any of these tools and frameworks will give you a competitive edge.

Advanced Data Skills

Another significant trend we’ve noticed in the past few years is the amplification of competencies required by data scientists in smaller firms. Companies often look for full-stack data experts with superb data analysis and ML skills and are also competent in cloud computing, data engineering, and architecture.

Of course, this is partially due to the interlinked nature of data science roles and hiring managers’ difficulty navigating this complex field. Still, it’s a noticeable trend that we cannot dismiss. So, we’ve grouped and presented some of the advanced data skills we encountered among the data scientist requirements.

A bar graph representing the percentage of data scientist job postings requiring the following data engineering skills: 30.4% SQL, 14.3% Azure, 11.7% Apache Spark, 7.7% big data, 7.1% Hadoop, 6.9% Docker, 4.7% data pipelines, 4.4% ETL, 4.4% data modeling, etc.

Some frequently mentioned data engineering skills and software include (among others):

  • Microsoft Azure: 28.5%
  • Apache Spark: 11.7%
  • Big data: 7.7%
  • Hadoop: 7.1%
  • Docker: 6.9%
  • Data pipelines: 4.7%
  • ETL: 4.4%
  • Data modeling: 4.4%

A bar graph representing the percentage of data scientist job postings requiring the following cloud skills and software: 28.5% Microsoft Azure, 19.7% AWS, 11.2% Apache Spark, 7.4% data management, 7% Apache Hadoop, 6.8% Docker, etc.

The most popular cloud skills and software mentioned in data science job offers include Microsoft Azure (28.5%), AWS (19.7%), Apache Spark (11.2%), data management (7.4%), Apache Hadoop (7%), Docker (6.8%), and Kubernetes (4%).

A bubble chart representing the percentage of data scientist job postings requiring the following cloud certification: 19.7% AWS, 3.9% Google Cloud, 1.4% Oracle Cloud, and 0.1% CompTIA Cloud+.

Lastly, some data architecture competencies also appeared in a small number of job offers, including cloud computing (4.8%), microservice architecture (2.9%), NoSQL databases (1.8%), etc.

A bar graph representing the percentage of data scientist job postings requiring the following data architecture skills: 4.8% cloud computing, 2.9% microservices, 1.8% NoSQL databases, 1.7% distributed systems, 0.2% MongoDB, and 0.1% RESTful APIs.

Where Do Data Scientists Work?

With mass layoffs and hiring freezes in the tech sector and beyond, which industries hire data scientists in 2024?

The most significant percentage of job offers is in Technology & Engineering (28.2%), followed by job postings from HR companies (19%) hiring for various industries. Data science is also gaining popularity in Health & Life Sciences (13%), Financial and Professional Services (10%), and Primary Industries & Manufacturing (8.7%).

A pie chart representing the percentage of data scientist job postings per industry: 28.2% Technology & Engineering, 19% Human Resources & Employment, 13.1% Health & Life Sciences, 10.3% Financial & Professional Services, 8.7% Primary Industries & Manufacturing, 0.8% 	Transportation & Logistics, 0.8% Commerce & Real Estate, and 19% other.

How Much Do Data Scientists Make?

According to Glassdoor, the average data scientist salary in the US is \$157,000, ranging from \$132,00 to \$190,000. Yet, our study reveals that the most commonly posted salary range is \$160,000–\$200,000 annually. But note that only 37.8% of job postings announced the salary.

A bar graph representing the percentage of data scientist job postings mentioning certain salary ranges: 8.5% between $160,000 and $200,000, 8.3% between $120,000 and $160,000, 6.2% between $80,000 and $100,000, 4.7% between $100,000 and $120,000, 4% between $60,000 and $80,000, 3.6% less than $60,000, and 2.5% more than $200,000.

This reveals the numerous factors and biases affecting salary range reports. While Glassdoor’s data comes from employees who volunteer this information, our source is the hiring companies who are much more likely to report renumerations when they’re considered attractive.

In addition, salaries vary significantly by years of experience.

Below are Glassdoor’s estimates of the median annual total pay for data scientists in the US based on years of experience:

  • 0-1 years: $117,276
  • 1-3 years: $128,403
  • 4-6 years: $141,390
  • 7-9 years: $152,966
  • 10-14 years: $166,818
  • 15+ years: $189,884

While most job postings in our research didn’t specify the required work experience, the most significant percentage was for mid-level positions.

A bar graph representing the percentage of data scientist job postings requiring certain years of experience: 7.3% require 2 to 4 years, 6.8% require 4 to 6 years, 3.7% require 0 to 2 years, 3.6% require 8+ years, and 2.2% require 6 to 8 years.

Of course, factors like industry, location, education, and company size also influence data science salaries.

Still, data science is a high-paying career with a growing trajectory. The average data scientist’s salary reported by Glassdoor in 2023 was $125,242/year, with entry-level positions starting at \$83,011/year.

Is There a Demand for Data Scientists in 2024?

A few years ago, data science was a fancy word with little meaning to the average person. Now, the world has a better understanding of what the field encompasses. With a growing awareness of the significance of data in all areas of work and life and the commercialization of AI solutions, the demand for jobs in data science is on the rise. But the job requirements are changing.

According to the U.S. Bureau of Labor Statistics, data scientist positions will continue to be among the fastest-growing jobs in 2024. The projected increase in job openings from 2022 to 2032 is 35%.

In addition, the World Economic Forum’s Future of Jobs 2023 report estimates that by 2027, the demand for AI and machine learning specialists will increase by 40%, and for data analysts, scientists, engineers, BI analysts, and other big data and database professionals will grow by 30%–35%.

The Data Scientist Job Market in 2024: Challenges, Opportunities, and Requirements

Our findings revealed some intriguing—although expected—data science job trends in 2024. While fundamental data analysis and programming skills are still the most required competencies, employers’ expectations have expanded to include more advanced specializations like cloud, data engineering, data architecture, and (mostly) AI-related tools.

All this demonstrates that data scientists are still very much in demand, but the requirements are shifting. With rising expectations and more opportunities for mid-level and experienced specialists, the barrier to entry is higher—but so are the career development opportunities.

The data analyst job is a great stepping stone to a data science career if you‘re new to the field. Our Data Analyst and Data Scientist Career Tracks will help you move from basics to advanced specialization.


Is data science still worth it in 2024?
Absolutely. Pursuing a career in data science remains a wise and lucrative decision in 2024. The future of data science is bright—with new opportunities that require a unique blend of skills ranging from analytics to AI model development. Reinforcing this positive outlook, the U.S. News & World Report ranking positions data science 4th in Best Technology Jobs, 7th in Best STEM Jobs, and 8th in 100 Best Jobs in 2024. These rankings highlight the robust demand for data science professionals and the field's significance in the current job market. With its combination of high demand, substantial salaries, and a pivotal role in shaping the future of technology and business, this career path offers immediate rewards and long-term potential.


Is data science oversaturated?
While the data science job market is more competitive than in its nascent stages a few years ago, calling it oversaturated would overlook the dynamic expansion and diversification the field has undergone. Today, it encompasses various specialties and roles—reflecting its integration into virtually every industry. Our comprehensive research of 1,000 job postings revealed employers often seek data scientists with such specialized competencies as AI, data engineering, and cloud solutions. This creates numerous career development opportunities for those willing to upskill.


Is it hard to find a job as a data scientist in 2024?
The data science job market is teeming with potential, but candidates must align their skillsets with the industry's evolving needs. We’ve noticed a clear trend toward specialization by analyzing the most sought-after skills in 2024. Whether it's machine learning, AI, or cloud computing, professionals focusing on mastering specific tools and technologies are likelier to stand out in the job search. With rising expectations and high demand for experienced specialists, the barrier to entry for beginners is high. If you‘re entirely new to the field, you might consider a data analyst job a stepping stone to a data science role. The career development opportunities afterward will be endless.


Is data science a good career choice?
Yes, data science is an excellent career choice. It ranks 4th in the U.S. News & World Report Best Technology Jobs, 7th in Best STEM Jobs, and 8th in 100 Best Jobs in 2024. These rankings are based on various factors, including median salary, employment rate, future job prospects, stress level, and work-life balance. And data science ranks exceptionally high in salary and career growth opportunities. Additionally, the data scientist’s work-life balance is generally considered favorable, with many roles offering flexibility, such as remote work options. Our research shows that 5% of job offers are for remote positions.


How is the job market right now for data scientists?
The data scientist job market in 2024 offers substantial opportunities with a clear trajectory for growth and advancement. Despite broader economic challenges, demand remains high, particularly for specialists leveraging machine learning and AI technologies. Lastly, salaries are attractive—reflecting data scientists’ critical role in shaping business strategies through data.


Aleksandra Yosifova

Blog author at 365 Data Science

Aleksandra is a Copywriter and Editor at 365 Data Science. She holds a bachelor’s degree in Psychology and is currently pursuing a Master’s in Cognitive Science. Thanks to her background in both research and writing, she learned how to deliver complex ideas in simple terms. She believes that knowledge empowers people and science should be accessible to all. Her passion for science communication brought her to 365 Data Science.