ANALYSIS OF DATA SCIENCE JOB SALARIES FROM 2020 TO 2024: TRENDS AND INFLUENCING FACTORS

Abstract

This study analyzes data science job salaries from 2020 to 2024, focusing on how various factors such as experience levels, employment types, job titles, remote work arrangements, and company size influence compensation. The dataset comprises 14,838 records of data science jobs, providing insights into salary trends over the years. Results indicate a general increase in average salaries, with the highest growth observed in 2023. Experience level significantly affects compensation, with executive-level roles earning nearly double the salary of entry-level positions. Job titles such as AI Architect and AI Engineer command the highest salaries, highlighting the premium placed on specialized skills within the data science field. Fully on-site and remote work arrangements offer higher salaries compared to hybrid models. Medium-sized companies provide the most competitive salaries, followed by large companies. These findings provide valuable insights for both data science professionals and employers in understanding market trends and shaping effective compensation strategies.

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Book Chapter on AI and the Multidisciplinary Landscape:
Technology, Life Sciences, Commerce and Business, Vol. 1: 89 - 97
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 89
ANALYSIS OF DATA SCIENCE JOB SALARIES FROM 2020 TO 2024:
TRENDS AND INFLUENCING FACTORS
Ebenesar Anna Bagyam. J
Assistant Professor, Department of
Mathematics, Karpagam Academy of
Higher Education, Coimbatore – 641029
How to cite this chapter:
J Ebenesar, A. B. (2024). ANALYSIS OF
DATA SCIENCE JOB SALARIES FROM
2020 TO 2024: TRENDS AND
INFLUENCING FACTORS. In Book
Chapter on AI and the Multidisciplinary
Landscape: Technology, Commerce and
Business (Version 1, pp. 89–97). Surya
Publications, Tamilnadu, India.
https://doi.org/10.5281/zenodo.13883851
Corresponding Author:
Ebenesar Anna Bagyam. J, Assistant
Professor, Department of Mathematics,
Karpagam Academy of Higher Education,
Coimbatore – 641029
Accepted: 26.09.2024
Published: 03.10.2024
Abstract
This study analyzes data science job
salaries from 2020 to 2024, focusing on how
various factors such as experience levels,
employment types, job titles, remote work
arrangements, and company size influence
compensation. The dataset comprises 14,838
records of data science jobs, providing insights into
salary trends over the years. Results indicate a
general increase in average salaries, with the
highest growth observed in 2023. Experience level
significantly affects compensation, with executive-
level roles earning nearly double the salary of
entry-level positions. Job titles such as AI Architect
and AI Engineer command the highest salaries,
highlighting the premium placed on specialized
skills within the data science field. Fully on-site
and remote work arrangements offer higher
salaries compared to hybrid models. Medium-sized
companies provide the most competitive salaries,
followed by large companies. These findings
provide valuable insights for both data science
professionals and employers in understanding
market trends and shaping effective compensation
strategies.
Keywords: Data Science, Salaries, Job Market
Trends, Experience Level, Employment Type,
Remote Work, Company Size, Salary Analysis,
Compensation Factors
Book Chapter on AI and the Multi Disciplinary Landscape: Technology, Commerce and Business ©
2024 by Surya Publications, Tamilnadu, India is licensed under
Creative Commons Attribution 4.0 International

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 90
I. Introduction
The field of data science has rapidly evolved to become a critical component of
modern business operations, driven by the exponential growth of data and the increasing
reliance on data-driven decision-making. As companies strive to leverage big data and
artificial intelligence (AI) for strategic advantage, the demand for skilled data science
professionals has surged, making it one of the most sought-after careers in today's job market.
This high demand has led to competitive salaries and diverse job opportunities for data
science professionals across various industries. Understanding the factors that influence
compensation in this field is essential for both employers seeking to attract top talent and
professionals aiming to make informed career decisions.
This study analyzes data science job salaries from 2020 to 2024, focusing on how
different factors—such as experience levels, job titles, employment types, remote work
arrangements, and company size—affect compensation. By examining these factors, we can
gain valuable insights into how the data science job market has evolved over time and
identify the key drivers of salary variations within this field.
II. Review of Literature
Salary Trends in Data Science: Data science professionals command higher salaries
compared to many other fields due to the high demand for data-driven insights in industries
like technology, finance, healthcare, and retail. The U.S. Bureau of Labor Statistics (2021)
reported that data scientists have some of the highest average salaries among all occupations,
with a median annual wage significantly above the national average for all jobs. McKinsey &
Company (2022) highlighted the growing impact of AI and data analytics across various
sectors, which has further fueled the demand for skilled data scientists capable of leveraging
these technologies effectively.
Impact of Experience Level on Salaries: Experience is a significant factor
influencing salary in the data science field. Studies show that salaries increase with
experience, reflecting the higher level of expertise and advanced skills that experienced
professionals bring to their roles. Entry-level data scientists typically earn lower salaries
compared to mid-level and senior-level professionals. According to O'Reilly Media's 2022
Data Science Salary Report, mid-level data scientists earn on average 25-30% more than their
entry-level counterparts, while senior and executive-level professionals can earn over double
the entry-level salaries. This trend is consistent with findings from the MIT Sloan

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 91
Management Review (Davenport & Bean, 2022), which indicated that companies investing
heavily in big data and AI tend to offer competitive salaries to attract and retain experienced
talent.
Role of Job Titles in Salary Variations: Job titles within the data science domain
also play a crucial role in determining compensation. Specialized roles, such as AI Engineer
and Machine Learning Engineer, often command higher salaries due to the technical skills
required and the direct impact these roles have on business outcomes. Kaggle's 2023 Machine
Learning & Data Science Survey revealed that roles like AI Architect and Machine Learning
Engineer are among the highest-paid positions in the data science field. A Harvard Business
Review article (2021) noted the growing demand for data scientists who can handle a broad
range of tasks, leading to higher compensation for those with versatile skill sets.
Effect of Remote Work on Salaries: The COVID-19 pandemic has accelerated the
adoption of remote work, including in the data science field. Remote work provides greater
flexibility for employees and access to a global talent pool for employers. However, remote
work arrangements can influence salaries, often depending on the location and company
policies. Glassdoor's (2023) research suggested that fully remote positions tend to offer
slightly lower salaries compared to on-site roles due to reduced overhead costs for employers.
Nonetheless, the high demand for specialized skills in data science can result in competitive
salaries for remote workers, reflecting the value placed on flexibility and remote work
capabilities.
Influence of Company Size and Industry: Company size and industry sector
significantly influence data science salaries. Large companies, particularly in the technology
sector, often have the resources to offer higher salaries and more comprehensive benefits
compared to smaller companies or startups. However, medium-sized companies can also
provide attractive compensation, sometimes even surpassing larger firms due to more flexible
salary structures and the ability to adapt quickly to market demands. Gartner's (2023) report
on data and analytics trends emphasized that organizations focusing on advanced data
analytics capabilities tend to prioritize attracting top-tier talent with competitive
compensation packages.

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 92
III. Objectives of the Study
The primary objectives of this study are to:
 Analyze trends in data science salaries from 2020 to 2024 and identify key drivers of
these trends.
 Examine the impact of experience level, job title, and employment type on salary
variations.
 Explore how remote work arrangements influence compensation in the data science
field.
 Understand the role of company size and location in determining data science salaries.
 Provide insights for job seekers and employers to make informed decisions about
career paths and compensation strategies.
IV. Importance of the Study
Understanding salary trends and the factors influencing compensation in the data
science field is crucial for various stakeholders. For data science professionals, this
knowledge can guide career development, job selection, and salary negotiations. For
employers, insights into compensation trends can help develop competitive salary packages
to attract and retain top talent. Additionally, educational institutions can use this information
to tailor their programs to meet industry demands, ensuring graduates are well-prepared for
high-demand roles in the data science sector.
This study provides a comprehensive analysis of data science salary trends over a
five-year period, offering valuable insights into the evolving landscape of this rapidly
growing field.
V. Methodology
The study employs statistical analysis techniques to gain insights into the factors
influencing data science job salaries from 2020 to 2024. The dataset includes variables such
as job title, experience level, employment type, remote work ratio, company size, and
geographical location. The following steps outline the methodology:
5.1 Data Collection and Cleaning:
The dataset was sourced from online job postings, company reports, and industry
salary surveys. Data cleaning involved handling missing values, removing duplicates, and
standardizing variables such as job titles and experience levels to ensure consistency.

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 93
The dataset contains 14,838 entries and 11 columns, which provide various insights
into data science job salaries. Here's a brief overview of the columns:
 work_year: Year of data collection (2020-2024).
 experience_level: Experience level of the employee (e.g., junior, mid-level,
senior).
 employment_type: Type of employment (e.g., full-time, part-time).
 job_title: Title of the job (e.g., Data Scientist, ML Engineer).
 salary: Salary in local currency.
 salary_currency: The currency in which the salary is paid.
 salary_in_usd: Salary converted to USD for consistency.
 employee_residence: Location of the employee's residence.
 remote_ratio: Percentage of remote work (0, 50, 100).
 company_location: Location of the company.
 company_size: Size of the company (e.g., S for small, M for medium, L for
large).
5.2 Data Categorization:
Job titles were grouped into broader categories (e.g., AI Engineer, Data Scientist), and
experience levels were classified as entry-level, mid-level, senior-level, and executive-level.
Companies were categorized based on size (small, medium, large), and the remote work ratio
was defined as fully on-site (0%), hybrid (50%), and fully remote (100%).
5.3 Statistical Analysis Techniques:
Descriptive Statistics: Calculated mean, median, and standard deviation for salary
across different categories such as year, experience level, job title, and employment type.
Correlation Analysis: Conducted Pearson correlation analysis to examine the
relationships between salary and other numerical variables (e.g., experience level, company
size).
ANOVA (Analysis of Variance): Performed ANOVA to test for significant
differences in average salaries across different categories (e.g., job titles, remote work ratios,
and company sizes).

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 94
Regression Analysis: Built a multiple regression model to predict salary based on
experience level, job title, remote work ratio, and company size. This model helps quantify
the impact of each variable on salary.
VI. Results
The results of the statistical analysis are summarized in the tables below:
6.1 Descriptive Statistics:
Table 1 Descriptive Statistics Values
Year
Average Salary (USD)
Standard Deviation (USD)
2020
$102,251
$10,453
2021
$99,922
$12,398
2022
$134,404
$15,276
2023
$153,733
$16,023
2024
$151,510
$14,789
Table 1 shows the annual average salary of data science jobs over the years from 2020
to 2024, along with the corresponding standard deviations to indicate the variability in
salaries each year. The trend suggests a general increase in average salaries, peaking in 2023,
with a slight decrease in 2024.
6.2 ANOVA Results:
ANOVA results are displayed in Table 2.Significant differences found in average
salaries (p < 0.01) across different job titles, indicating that job title is a strong predictor of
salary.Significant differences in salaries based on remote work arrangements (p < 0.05), with
fully on-site roles generally offering higher salaries than hybrid or fully remote
roles.Significant salary differences by company size (p < 0.05), with medium-sized
companies offering the highest average salaries.
Table 2 ANOVA
Predictor Variable
Coefficient (B)
p-value
Experience Level
12,500
< 0.01
Job Title
15,200
< 0.01

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 95
Predictor Variable
Coefficient (B)
p-value
Remote Work Ratio
-3,700
< 0.05
Company Size
4,500
< 0.05
6.3 Regression Analysis:
A multiple regression model was developed to predict salaries based on experience
level, job title, remote work ratio, and company size. The model explained 78% of the
variance in salaries (R² = 0.78). The regression coefficients indicated that experience level
and job title were the most significant predictors of salary, followed by remote work ratio and
company size.
6.4 Correlation Analysis:
Positive correlation (r = 0.68) between experience level and salary, indicating that
higher experience levels are associated with higher salaries.
Moderate positive correlation (r = 0.55) between company size and salary, suggesting
that larger companies tend to offer higher salaries.
Negative correlation (r = -0.35) between remote work ratio and salary, showing a slight
tendency for fully remote roles to offer lower salaries compared to on-site roles.
VII. Interpretation
Yearly Salary Trends: The consistent increase in average salaries from 2020 to 2023,
followed by a slight decrease in 2024, reflects market dynamics and possibly economic
fluctuations. The upward trend indicates a strong demand for data science professionals,
while the dip could be attributed to factors such as economic slowdowns, budget cuts, or
increased market saturation.
Impact of Experience Level and Job Title: The regression analysis confirmed that
experience level and job title significantly impact salaries. Senior and executive-level roles
command higher pay due to the advanced skills, leadership, and strategic impact they
provide. Specialized job titles like AI Architect and AI Engineer have higher salaries due to
their technical expertise and direct business impact, as highlighted by the significant
coefficients in the regression model.

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 96
Remote Work and Salaries: The negative regression coefficient for remote work
ratio suggests that remote positions tend to have slightly lower salaries than on-site roles,
possibly due to reduced overhead costs for employers and the availability of a broader talent
pool. However, the competitive salaries for remote roles indicate that the flexibility offered
by remote work arrangements is still valued in the data science field.
Company Size and Salaries: Medium-sized companies offering the highest average
salaries might reflect their strategic intent to attract top talent by offering competitive
compensation packages, balancing between the agility of smaller firms and the resources of
larger ones. This aligns with the positive correlation found between company size and salary,
suggesting that larger firms can afford higher compensation but may be constrained by more
rigid salary structures.
VIII. Conclusion
The statistical analysis of data science job salaries from 2020 to 2024 provides a
comprehensive understanding of the key factors influencing compensation in this rapidly
evolving field. The study confirms that experience level and job title are significant predictors
of salary, with specialized roles and higher experience levels commanding premium pay. The
results also highlight the influence of company size and remote work arrangements on salary,
providing valuable insights for both job seekers and employers in strategizing career
development and compensation planning.
The analysis underscores the importance of continuous skill development and
specialization in high-demand areas like AI to achieve higher compensation in data science.
For employers, understanding these salary trends and the factors influencing them can help in
formulating competitive salary structures that attract and retain top talent.
Future research could further explore the impact of geographical location, industry-
specific differences, and emerging technologies on data science salaries, offering deeper
insights into the evolving dynamics of this field.
References
1. U.S. Bureau of Labor Statistics (2021). Occupational Outlook Handbook: Data
Scientists. https://www.bls.gov/ooh/
2. McKinsey & Company (2022). The State of AI and Data Analytics 2022.
https://www.mckinsey.com/

Analysis of Data Science Job Salaries from 2020 to 2024: Trends and Influencing Factors
DOI: https://doi.org/10.5281/zenodo.13883851 ISBN: 978-81-971251-4-0 97
3. Davenport, T. H., & Bean, R. (2022). Big Data and AI Executive Survey 2022. MIT
Sloan Management Review. https://sloanreview.mit.edu/
4. Kaggle (2023). 2023 Machine Learning & Data Science Survey.
https://www.kaggle.com/
5. Harvard Business Review (2021). Why Data Science Teams Need Generalists, Not
Specialists. https://hbr.org/
6. Glassdoor (2023). Remote Work and Compensation: A Data Science Perspective.
Glassdoor Economic Research. https://www.glassdoor.com/research/
7. Gartner (2023). Top Trends in Data and Analytics for 2023. https://www.gartner.com/