Data Science Careers: What You Can Do With a Data Science Degree

Data science careers rank among the fastest-growing and highest-paying paths in today’s job market. The field combines statistics, programming and business judgment in a single career track.

Programs like the Master of Science (M.S.) in Data Science and Artificial Intelligence online program at Campbellsville University are built around that exact combination. That combination is what drives the demand and pay detailed below.

Employment of data scientists is projected to grow 35% from 2025 to 2035. That figure comes from the U.S. Bureau of Labor Statistics (BLS) and far outpaces most occupations. The median annual wage for the role reached $120,230 in May 2025.

Employers keep creating new data science jobs as they invest more in artificial intelligence. For professionals weighing a graduate degree, that growth translates into real career options across several specializations.

These are national figures; actual pay varies by employer, location and experience level. The profiles below cover what drives that range for each path.

6 Career Paths in Data Science

A data science degree opens doors to six distinct career paths. Each path has its own focus and pay range. Some graduates build predictive models, while others design the systems that feed those models. Still others translate technical findings into business strategy.

The profiles that follow list each path’s core duties and national salary range. Several also point to the Campbellsville coursework that connects most directly to that path.

Data Scientist

A data scientist applies statistical models and machine learning to complex datasets. The goal is to uncover patterns and forecast outcomes other roles would miss. According to Robert Half, the national salary range for data scientists spans $121,750 to $182,500. That figure comes from the firm’s 2026 Salary Guide.

The guide lists strong statistical modeling skills as a core requirement for the role. Programming languages such as Python and SQL round out the typical skill set. This path suits graduates who enjoy building models from raw, messy data. Campbellsville’s capstone project gives students hands-on practice with exactly that kind of applied modeling work.

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models at scale. Engineers in this role often take a data scientist’s prototype and move it into production. Machine learning engineer salary figures from Robert Half’s 2026 guide place the national range between $134,000 and $193,250.

That premium reflects how central these engineers have become to companies scaling AI products. Demand has grown as more companies invest in generative AI. Strong software engineering skills matter here as much as statistics. Campbellsville’s introductory AI and machine learning coursework gives students early, hands-on exposure to the skills this path depends on.

AI Research Scientist

An AI research scientist develops new algorithms and methods rather than applying existing ones. The closest published federal group is computer and information research scientists. That group typically requires a master’s degree in computer science or a related field. That requirement comes from BLS. Its median annual wage reached $140,300 in May 2025, with 22% projected growth from 2025 to 2035.

A doctoral degree is required for some positions in this occupation, according to the Occupational Information Network (O*NET). A degree like Campbellsville’s meets that more common master’s-level requirement. It can still open doors to applied research roles that support broader AI initiatives.

Data Engineer

Data engineers build and maintain data pipelines and systems. Those systems move raw data into a usable form for analysis. Robert Half’s 2026 guide lists a national salary range of $127,000 to $180,750 for the role. The guide describes data engineers as a bridge between business users and data scientists.

BLS tracks a related group, database administrators and architects. Database architects in that group earned a median of $139,500 in May 2025. This path fits graduates who prefer building reliable systems over building predictive models. Campbellsville’s big data analytics and data warehousing coursework builds the same pipeline skills this path requires.

Business Intelligence Analyst

Business intelligence turns existing data into dashboards, reports and strategic recommendations. According to Robert Half’s 2026 guide, business intelligence analysts earn a national range of $69,000 to $104,000. That is a more accessible entry point than the technical engineering paths above.

The role suits graduates who want to translate data into decisions. It does not require building the underlying models from scratch. Strong communication skills matter as much as technical ones in this role.

Quantitative Analyst

Quantitative analysts apply mathematical models to problems in finance, risk management or pricing. BLS does not track this title directly. Instead, it groups similar analytical work under operations research analysts. That group carried a median annual wage of $88,940 in May 2025.

That group is also projected to grow 12% from 2025 to 2035. Columbia University’s operations research program describes this kind of work as an applied science. That work focuses on quantitative decision problems, such as how to allocate limited resources. Quantitative analysts often apply that same optimization toolkit to work such as trading and corporate risk analysis.

What’s the Difference Between Data Science and Data Analysis?

A data analyst answers defined questions using existing data. A data scientist builds models that predict what happens next instead. A data analyst might report which product line sold best last quarter.

A data scientist instead builds a model that forecasts next quarter’s demand. This distinction matters for anyone comparing a data science master’s degree to a simpler analytics certificate.

A data analysis credential can prepare someone to interpret existing reports well. A data science degree goes further, building the statistical and programming foundation needed to design new models.

Industries Hiring Data Science and AI Talent

Data science talent is in demand across nearly every sector, but a few industries hire most aggressively. Technology companies remain the largest employer of data scientists and machine learning engineers. Healthcare organizations rank close behind, using data science for clinical research and genomics.

Finance, pharmaceutical companies and e-commerce round out the industries competing hardest for this talent. In finance, data scientists often support fraud detection and risk pricing. In pharmaceutical research, they often support drug discovery and clinical trial analysis. In e-commerce, they often support personalization and demand forecasting.

The World Economic Forum’s Future of Jobs Report 2025 tracked the fastest-growing jobs worldwide. Big data specialists, fintech engineers and AI and machine learning specialists topped that list. That level of demand rewards candidates with specialized, technical training in these exact skill areas.

Recent survey data shows most American workers are already thinking about how to future-proof their careers against AI. Specialized data science roles are one clear example of that shift. Graduates of programs like Campbellsville’s M.S. in Data Science and Artificial Intelligence are well-positioned to compete for roles in any of these fast-growing specialties.

How Does Campbellsville’s M.S. in Data Science and AI Map to These Career Outcomes?

The M.S. in Data Science and Artificial Intelligence at Campbellsville University is a 36-credit-hour, fully online program. It is built around the same skills that show up across the six data science careers above.

Coursework covers machine learning, big data analytics, data mining, data warehousing and AI ethics. A capstone project rounds out the program and mirrors the applied work employers expect from new hires. Faculty bring direct AI and data science experience into the program. Each student also works with a personal academic advisor throughout the program.

That range of coursework is why the program supports more than one destination. A graduate who focuses on modeling coursework and the capstone project builds toward data scientist roles. That same focus also supports machine learning engineer careers. Those who lean into data pipeline coursework are better positioned for data engineering instead.

Statisticians and data scientists are in demand more than ever to transform growing volumes of data into insight. The American Statistical Association supports that assessment. A STEM-designated program built around that exact skill set gives graduates a direct path into that demand.

Choosing the Right Data Science Career Path

Data science now spans six distinct career paths, from hands-on model building to systems architecture to business-facing analysis, each with its own required skills and national pay range. The best fit depends on whether someone prefers building predictive models, engineering the systems that support them, or translating findings into business strategy. A dedicated master’s program that covers machine learning, big data analytics and applied AI ethics prepares graduates to move into any of these six paths rather than locking them into just one.

Campbellsville’s M.S. in Data Science and Artificial Intelligence is one option built around that same range of skills. Readers weighing a broader computer science foundation first can compare notes in Campbellsville’s guide to computer science career paths and salaries.

Explore the M.S. in Data Science and AI at Campbellsville University to match your career path to the right coursework.

Frequently Asked Questions About Data Science Careers

The six career paths above raise a few common follow-up questions. These answers cover the practical details readers ask most before choosing a path or a program.

What is the highest-paying data science career path?

Machine learning engineer roles carry the highest national salary range among the six paths, from $134,000 to $193,250 according to Robert Half’s 2026 Salary Guide.

Do you need a doctoral degree to become a data scientist?

No. Most data science roles, including data scientist and machine learning engineer positions, typically call for a master’s degree rather than a doctorate; O*NET lists a doctoral degree as a requirement for only some positions in specialized research roles.

How long does it take to complete a data science master’s degree?

A program like Campbellsville’s 36-credit-hour M.S. in Data Science and Artificial Intelligence can be completed in about 12 months of full-time, online study.

Which industries hire the most data science talent?

Technology companies hire the most data scientists and machine learning engineers, with healthcare, finance, pharmaceutical and e-commerce companies close behind.

About Campbellsville University’s Online M.S. in Data Science and AI

Campbellsville University was founded in 1906. It has since grown to enroll more than 12,000 students, many of them pursuing graduate study online. The university is accredited by the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC).

The program accepts up to 12 transfer credits and requires a minimum undergraduate GPA of 2.5, with no Graduate Record Examination (GRE) or Graduate Management Admission Test (GMAT) scores needed. Campbellsville also holds a Military Friendly School designation and keeps class sizes small, giving students direct, one-on-one interaction with faculty throughout the program.