25 Highest-Paying Data Science & Analytics Jobs

Introduction

Data has moved from being a support function to becoming a core business asset. Companies use data to determine what products to build, which customers to target, how to price services, where to invest, how to reduce risk, and how to develop artificial intelligence systems.

That shift has created a much broader range of high-paying careers than the traditional data analyst or data scientist roles many people associate with the field. Today, professionals can build careers in data science, machine learning, artificial intelligence, data engineering, analytics leadership, quantitative finance, data architecture, governance, and data strategy.

The compensation can be substantial, particularly for experienced professionals working in technology, financial services, consulting, and large enterprises. Current compensation data from Built In, for example, puts the average U.S. base salary for AI Engineers at $184,757 and Machine Learning Engineers at $162,080, while senior data scientists average $151,120 in base salary.

This guide examines 25 of the highest-paying data science and analytics careers, including typical pay ranges, what professionals do, the skills they need, and how to prepare for the next step.

How We Compared Data Science and Analytics Salaries

Salary comparisons in data and technology are complicated because employers use different titles for similar positions, while the same title can involve very different responsibilities.

For established occupations, this article uses U.S. Bureau of Labor Statistics data where available. For newer technology roles, including AI Engineer, Machine Learning Engineer, and certain data leadership positions, current compensation information and advertised salary ranges from Built In are also considered.

The salary ranges below should be viewed as general U.S. market ranges rather than guaranteed compensation. Location, experience, industry, employer, education, specialization, bonuses, and equity can have a major effect on actual earnings.

This distinction is particularly important in AI. Base salary can be substantially different from total compensation at major technology companies.

Why Data Science and Analytics Careers Pay So Well

The economics are straightforward: organizations are willing to pay for professionals who can solve expensive problems.

A data scientist who improves customer retention can influence millions of dollars in revenue. A machine learning engineer can help automate a process previously requiring thousands of employee hours. A data architect can design the infrastructure supporting an entire enterprise. An AI engineer can help turn a new technology into a commercial product.

The demand is reflected in government employment projections. The U.S. Bureau of Labor Statistics projects 34% employment growth for data scientists from 2024 to 2034, with about 23,400 openings annually. The highest-paid 10% of data scientists earned more than $194,410 in May 2024.

AI is also changing the broader technology employment landscape. BLS expects increasing adoption of artificial intelligence to contribute to demand for computer and mathematical occupations.

25 Highest-Paying Data Science & Analytics Jobs

1. Chief Data Officer

Typical compensation: $180,000–$350,000+

The Chief Data Officer is an executive responsible for turning data into an organizational asset. Responsibilities can include data strategy, governance, data quality, analytics, privacy, information management, and increasingly AI readiness.

This is not an entry-level data career. CDOs generally arrive after years of experience in data science, analytics, technology, information management, consulting, or executive leadership.

At the executive level, compensation can also include bonuses, long-term incentives, and equity, making total compensation substantially higher than base salary.

2. Director of Data Science

Typical compensation: $150,000–$275,000+

Directors of Data Science lead teams responsible for advanced analytics, predictive modeling, machine learning, and data-driven products.

The position combines technical knowledge with management and business strategy. A director may spend less time building models personally and more time deciding which problems the organization should solve, allocating resources, hiring talent, and communicating results to executives.

3. Principal Data Scientist

Typical compensation: $160,000–$275,000+

Principal Data Scientists are highly experienced individual contributors who tackle complex analytical and technical problems.

They may design advanced models, establish technical standards, mentor other data scientists, and influence major product or business decisions.

Current compensation data illustrates how high the ceiling can become at this level. Built In reports individual principal data scientist compensation examples exceeding $200,000, including a reported $250,000 salary in Seattle.

4. AI Engineer

Typical base salary: $80,000–$338,000

AI engineering is one of the most compelling emerging career paths in the data and technology market.

AI Engineers build applications and systems incorporating artificial intelligence, machine learning, generative AI, natural language processing, and related technologies.

Built In currently reports an average U.S. base salary of $184,757 and average total compensation of $211,243 for AI Engineers. The reported salary range extends from $80,000 to $338,000. In San Francisco, the reported average base salary is $246,250.

This is one of the clearest examples of why AI deserves significant attention in a modern data-career guide.

5. Machine Learning Engineer

Typical base salary: $70,000–$318,000

Machine Learning Engineers combine software engineering, data, and machine learning.

They develop systems that train, deploy, monitor, and improve machine learning models. Their work is particularly important when organizations move AI projects from experimentation into production.

Built In reports an average U.S. base salary of $162,080, with average total compensation of $212,022. The reported range is $70,000 to $318,000. San Francisco Machine Learning Engineers average $207,474 in base salary.

6. Computer and Information Research Scientist

Typical compensation: $100,000–$230,000+

Computer and Information Research Scientists work on advanced computing problems involving algorithms, artificial intelligence, machine learning, robotics, and other emerging technologies.

BLS reported a May 2024 median wage of $140,910 for computer and information research scientists. The occupation typically requires advanced education, particularly for research-oriented positions.

This career is particularly attractive to professionals interested in the research side of AI rather than solely its business applications.

7. Applied Scientist

Typical base salary: $125,000–$275,000+

Applied Scientists use research and advanced analytical techniques to solve practical business and technology problems.

The role can involve machine learning, experimentation, causal inference, recommendation systems, forecasting, search, or generative AI.

Current job postings demonstrate the range. Adobe has advertised Applied Scientist positions with U.S. salary ranges reaching $257,600 and, for certain advanced roles, $270,950. Thumbtack has advertised Bay Area Applied Scientist positions reaching $275,000.

8. Research Scientist

Typical compensation: $120,000–$300,000+

Research Scientists work on new models, algorithms, computational techniques, and scientific approaches to difficult problems.

Within technology companies, research scientist positions can focus on AI, machine learning, computer vision, natural language processing, or other areas of advanced computing.

The highest compensation tends to occur among experienced researchers with specialized expertise, strong technical backgrounds, and experience working on commercially valuable problems.

9. Data Architect

Typical base salary: $100,000–$290,000

Data Architects design the infrastructure organizations use to store, integrate, organize, secure, and access information.

The role has become especially important as businesses build cloud data platforms and AI systems that depend on reliable enterprise data.

Built In reports an average U.S. base salary of $146,200, average additional cash compensation of $42,384, and a reported salary range extending to $290,000. San Francisco's average base salary is reported at $168,645.

10. Data Engineering Manager

Typical base salary: $110,000–$255,000

Data Engineering Managers lead teams responsible for data infrastructure, pipelines, platforms, databases, and data architecture.

They combine technical expertise with people management and strategic planning.

Built In reports an average U.S. base salary of $164,070 and average total compensation of $191,727, with reported salaries ranging from $110,000 to $255,000.

The location effect can be significant. Built In reports an average San Francisco base salary of $189,801 for Data Engineering Managers.

11. Senior Data Scientist

Typical base salary: $117,000–$350,000

Senior Data Scientists work on more complex analytical problems and often have substantial influence over modeling, experimentation, product decisions, and business strategy.

Built In reports an average U.S. base salary of $151,120 and average total compensation of $176,357. Its reported salary range extends from $47,000 to $396,000, illustrating the enormous variation created by experience, employer, and location.

For readers comparing careers, the BLS data provides useful context: the median data scientist wage was $112,590 in May 2024, while the highest-paid 10% earned more than $194,410.

12. Quantitative Analyst

Typical compensation: $100,000–$300,000+

Quantitative Analysts apply mathematics, statistics, programming, and financial modeling to investment, trading, risk management, and other financial problems.

Quant careers can be particularly lucrative in investment banking, hedge funds, proprietary trading, asset management, and financial technology.

The field is highly competitive and usually requires strong mathematical and programming ability. Professionals who combine quantitative expertise with specialized financial knowledge can have significant earning potential.

13. Director of Analytics

Typical base salary: $130,000–$345,000

Directors of Analytics establish and oversee an organization's analytics strategy.

They may lead analysts, data scientists, business intelligence professionals, and other data teams while working directly with executives.

Built In reports an average U.S. base salary of $161,810, average total compensation of $192,616, and a reported salary range reaching $345,000.

The role demonstrates that high-paying data careers do not necessarily require spending an entire career writing code.

14. Data Science Manager

Typical compensation: $140,000–$275,000+

Data Science Managers oversee teams of data scientists and help determine which analytical projects deserve investment.

They need enough technical expertise to evaluate models and methodologies, but leadership, communication, hiring, project management, and business judgment become increasingly important.

Experienced managers can eventually move into director, vice president, or chief data leadership positions.

15. Machine Learning Scientist

Typical compensation: $130,000–$300,000+

Machine Learning Scientists focus on developing and improving machine learning techniques and models.

The role is often more research-oriented than that of a Machine Learning Engineer. Professionals may work on model architecture, experimentation, statistical methods, optimization, and evaluation.

A strong background in mathematics, statistics, computer science, and machine learning is generally valuable.

16. Data Product Manager

Typical compensation: $120,000–$180,000+

Data Product Managers oversee products and features built around data, analytics, or AI.

They act as a bridge between business stakeholders and technical teams, defining priorities, understanding users, managing roadmaps, and measuring business impact.

Current job postings show substantial variation. Turquoise Health lists $127,600–$145,000 for a Data Product Manager, while Findem has advertised a senior Data Product Manager position at $160,000–$180,000.

17. Business Intelligence Director

Typical compensation: $130,000–$250,000+

Business Intelligence Directors oversee systems and teams that transform organizational information into dashboards, reporting, metrics, and decision-support tools.

The role can be particularly attractive to professionals who enjoy data but also want to work closely with business leadership.

Strong knowledge of business intelligence platforms, data modeling, visualization, SQL, and executive communication can be valuable.

18. Senior Data Engineer

Typical base salary: $90,000–$343,000

Senior Data Engineers design and maintain the infrastructure that supports analytics, AI, and data science.

Built In reports an average U.S. base salary of $143,076, average total compensation of $164,737, and a reported range extending to $343,000. San Francisco's reported average base salary is $203,079.

This is one of the strongest career options for professionals who enjoy programming and systems more than statistical modeling.

19. Statistician

Typical salary range: $60,000–$170,000+

Statisticians design studies, analyze data, develop statistical models, and interpret uncertainty.

The career remains relevant across healthcare, government, finance, research, manufacturing, and technology.

BLS reports a $103,300 median annual wage for statisticians in May 2024. The highest-paid 10% earned more than $170,700.

Advanced statistical training can also provide a strong foundation for moving into data science, machine learning, or quantitative analysis.

20. Actuary

Typical salary range: $75,000–$206,000+

Actuaries use mathematics, statistics, and financial theory to analyze risk and uncertainty.

The profession is particularly important in insurance but also extends into consulting, finance, healthcare, and enterprise risk management.

BLS reports a $125,770 median annual wage for actuaries. The highest-paid 10% earned more than $206,430 in May 2024. Employment is projected to grow 22% from 2024 to 2034.

Actuarial careers are also unusual because professional certification exams are a major part of career advancement.

21. Operations Research Analyst

Typical salary range: $54,000–$159,000+

Operations Research Analysts use mathematics, statistics, and logic to solve complex business and operational problems.

They can work in logistics, supply chain management, defense, manufacturing, transportation, finance, and technology.

BLS reports a $91,290 median annual wage, with the highest-paid 10% earning more than $159,280. Employment is projected to grow 21% from 2024 to 2034.

Some specialized positions can pay considerably more. For example, a current KBR position in California lists $127,000–$190,000.

22. Analytics Manager

Typical base salary: $100,000–$302,000

Analytics Managers lead teams that use data to answer business questions and improve organizational performance.

They may oversee reporting, visualization, business intelligence, forecasting, customer analytics, and other analytical functions.

Built In reports an average U.S. base salary of $118,979 and average total compensation of $136,600, with a reported range extending to $302,000.

San Francisco is among the higher-paying markets, with Built In reporting an average base salary of $158,658.

23. Data Governance Manager

Typical compensation: $100,000–$232,000

Data Governance Managers oversee policies and processes that determine how organizations manage data quality, ownership, access, definitions, privacy, compliance, and accountability.

The role has become increasingly important as organizations adopt AI and need trustworthy data.

Current U.S. job postings show the potential range. PwC has advertised a Data Governance Manager position with a $99,000–$232,000 salary range, while Cox Enterprises has listed $111,500–$185,900.

24. Data Strategy Consultant

Typical compensation: $120,000–$250,000+

Data Strategy Consultants help organizations determine how to use data more effectively.

They may develop data strategies, assess data maturity, design governance programs, evaluate architecture, identify AI opportunities, and build transformation roadmaps.

The career is particularly attractive to professionals who combine data expertise with consulting, communication, and business strategy.

Because compensation varies considerably by consulting firm, seniority, geography, and client responsibilities, prospective candidates should evaluate individual job postings rather than relying on a single national salary number.

25. Big Data Engineer

Typical base salary: $103,000–$227,000

Big Data Engineers design systems capable of processing large and complex datasets.

They work with data pipelines, data warehouses, distributed systems, cloud platforms, data modeling, and analytics infrastructure.

Built In reports an average U.S. base salary of $151,131, average total compensation of $170,223, and a reported range of $103,000–$227,000.

The role is particularly relevant as companies build increasingly sophisticated AI and analytics environments that depend on large-scale data infrastructure.

What Skills Do High-Paying Data Professionals Need?

The exact skills vary by career, but several capabilities appear repeatedly across the highest-paying positions.

Programming: Python and SQL are particularly valuable across data science, analytics, engineering, and AI.

Statistics: Statistical reasoning is fundamental to data science, experimentation, forecasting, and quantitative analysis.

Machine learning: Professionals pursuing AI and advanced data science careers increasingly need to understand model development, evaluation, and deployment.

Data engineering: Data pipelines, cloud platforms, data warehouses, databases, and data architecture become increasingly important at senior levels.

Business knowledge: Technical ability has limited value if a professional cannot connect an analysis to a business decision.

Communication: Senior data professionals need to explain complex findings to executives, customers, and colleagues who may not have technical backgrounds.

Leadership: Management and executive careers require hiring, prioritization, stakeholder management, strategic planning, and organizational influence.

How to Start a Data Science or Analytics Career

Step 1: Build the fundamentals

Start with statistics, analytical thinking, spreadsheets, SQL, and basic programming.

You do not need to become an expert in everything immediately. A strong foundation is more valuable than trying to learn every technology at once.

Step 2: Choose a direction

The data field is too broad to master all at once.

If you enjoy statistics and modeling, consider data science.

If you enjoy programming and infrastructure, consider data engineering.

If you are interested in AI applications, explore machine learning or AI engineering.

If you enjoy business strategy, analytics management or data product management may be a better fit.

Step 3: Build practical experience

Work with real datasets and solve actual problems.

Build dashboards, analyze customer behavior, create predictive models, develop data pipelines, or experiment with machine learning.

Practical work demonstrates that you can apply your knowledge rather than simply describe it.

Step 4: Develop a specialization

Specialization can make your skills more valuable.

Potential areas include:

• Artificial intelligence
• Machine learning
• Generative AI
• Financial analytics
• Healthcare analytics
• Data engineering
• Data governance
• Business intelligence
• Quantitative finance
• Data architecture

Step 5: Continue developing professionally

The technology changes quickly. Professionals who remain competitive continually update their knowledge.

Certificates, structured courses, graduate education, professional projects, and hands-on experience can all play a role depending on the career path.

Data Science & Analytics Career Path

The path is not necessarily linear. A Data Analyst can move into data science, business intelligence, product management, or analytics leadership. A software engineer can transition into machine learning. A statistician can move into data science or quantitative analysis.

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Is a Data Science or Analytics Career Worth It?

For professionals who enjoy technology, mathematics, business problem-solving, or analytical work, data science and analytics can offer an unusually broad range of career opportunities.

But the high salaries should not create the impression that six-figure compensation is automatic.

The highest-paying roles generally require years of experience, specialized skills, strong technical capabilities, or leadership responsibility. Competition can also be intense, particularly for AI and machine learning positions.

The advantage is that there are multiple ways into the field.

You do not have to start as a machine learning engineer. You might begin in analytics and progress toward data science. You might enter through software development and specialize in AI. You might come from finance and develop quantitative expertise. Or you might build business and data skills and eventually move into data strategy or leadership.

Why Data Careers Should Remain on Your Radar

The most important takeaway is that "data science" is no longer one career.

It is an ecosystem.

AI Engineers, Machine Learning Engineers, Data Scientists, Data Engineers, Data Architects, Analytics Managers, Quantitative Analysts, and Data Governance professionals solve different problems, but they increasingly work within the same data-driven technology environment.

The BLS projects data scientist employment to grow 34% between 2024 and 2034, while the broader mathematical occupations group has a median annual wage well above the median for all U.S. occupations.

For someone considering a career change, that breadth is significant. You can build a career around technical development, research, business analytics, AI, infrastructure, risk, consulting, or leadership.

Building Your Data Science & Analytics Career

Start with the part of the field that fits your strengths.

If you enjoy mathematics and statistics, explore data science, statistics, actuarial work, or quantitative analysis. If you prefer programming and systems, consider data engineering, machine learning engineering, or AI engineering. If you enjoy working with executives and business teams, analytics management, data product management, consulting, or data strategy may be a better fit.

Then build the skills that employers actually use, develop practical experience, and gradually specialize.

The highest-paying data careers are rarely based on one skill. They are built by combining technical expertise, business understanding, communication, and the ability to turn data into decisions.

Continue Your Career Development

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About the Business Training Media Editorial Team

This article was researched and written by the Business Training Media Editorial Team. We publish expert content covering business strategy, leadership, workplace skills, artificial intelligence, cybersecurity, compliance, career development, online learning, professional certifications, business software, and organizational excellence. Our goal is to provide practical, research-backed insights that help professionals, business leaders, and organizations make informed decisions.