Top 10 Careers You Can Pursue With a Degree in Data Science

A data science degree opens more doors than the job title "data scientist" alone suggests. The combination of statistics, programming, and analytical thinking you build in a data science program translates directly into a wide range of careers across tech, finance, healthcare, and business.

This article walks through 10 careers that draw on a data science education, ranked by median pay. Each entry covers what the role actually involves day to day, current salary and growth data, and how well the position lines up with a typical data science curriculum.

What Can You Do With a Data Science Degree?

A data science degree, part of the broader data analytics field, trains you to collect data, analyze it, and turn that analysis into decisions, which is a skill set organizations need in nearly every industry. Graduates move into roles that range from building predictive models and machine learning systems to designing the databases and pipelines that keep data flowing, and even into business-facing positions that translate technical findings for nontechnical audiences.

The breadth of this skill set is exactly why data science graduates land in such different corners of the job market. Some roles lean heavily on programming and engineering, others lean on advanced statistics and theory, and still others focus on communicating insights and driving business strategy. That range gives graduates real flexibility to find a career that matches their specific strengths and interests, rather than being locked into one narrow job title.

Top 10 Careers for Data Science Graduates

The 10 careers below are ranked by median annual wage, from highest to lowest paying. Each one draws on core data science skills, though the exact mix of coding, statistics, and business acumen varies from role to role.

1. Computer and Information Research Scientist

Computer and information research scientists tackle some of the most complex problems in computing, designing new approaches to artificial intelligence, algorithms, and computing infrastructure. Much of the work is research-driven, often taking place at technology companies, government labs, or universities rather than in a typical corporate setting.

This role carries a median annual wage of $140,300, according to the Bureau of Labor Statistics, with employment projected to grow 22% through 2035 and about 2,900 openings projected each year. A data science bachelor's degree provides a strong foundation for this path, though many employers in this field prefer or require a master's or doctoral degree given the research-heavy nature of the work.

2. Machine Learning Engineer

This role takes the models data scientists build and turns them into production systems that run reliably at scale, which means it leans more toward software engineering than pure data analysis. Day-to-day work often involves writing and maintaining code, optimizing model performance, and collaborating closely with software development teams to deploy machine learning features into live products.

The Bureau of Labor Statistics doesn't track machine learning engineer as its own occupation, instead folding this kind of work into the broader Software Developers, Quality Assurance Analysts, and Testers category, which carries a median annual wage of $134,040 and a projected growth rate of 10% through 2035. Data science graduates who want to move into this role typically need to build stronger software engineering skills than a typical data science curriculum covers on its own, often through additional coursework or hands-on project work.

3. Actuary

Actuaries use math, statistics, and financial theory to analyze the economic costs of risk and uncertainty, work that's central to insurance, pension planning, and financial risk management. The role involves building models to estimate the likelihood of future events, then translating those estimates into policies and pricing that help organizations manage financial exposure.

Actuaries earn a median annual wage of $130,000, with employment projected to grow 9% through 2035 and about 1,500 openings projected each year. A data science degree builds the statistical foundation this career requires, though actuaries also need to pass a series of professional exams administered by actuarial societies, a certification path that runs alongside, rather than replaces, a traditional degree.

4. Data Engineer

Behind every data scientist's analysis is infrastructure someone had to build, and that's where data engineers come in, designing and maintaining the pipelines that move, store, and process data at scale. The work is closer to software engineering than data analysis, involving databases, cloud platforms, and the systems that keep data flowing reliably across an organization.

Because data engineering isn't tracked as its own BLS occupation, the closest available classification is Database Administrators and Architects, which carries a median annual wage of $126,760 and a projected growth rate of 4% through 2035, slower than most other roles on this list. A data science degree provides useful grounding in how data gets used downstream, though aspiring data engineers typically need to build deeper skills in database design and cloud infrastructure than a typical program covers.

5. Data Scientist

Data scientists gather data, build models to find patterns and make predictions, and turn that analysis into recommendations that guide business decisions. It's the most direct match for a data science degree, drawing on the full range of skills the curriculum is designed to teach, from statistics and programming to data visualization and communication.

This role carries a median annual wage of $120,230, with employment projected to grow 35% through 2035, the fastest growth rate of any career on this list, and about 24,800 openings projected each year. The strong alignment between this role and a typical data science curriculum makes it a natural first stop for many graduates, even those who eventually move into one of the other careers on this list.

6. Statistician

This career applies statistical theory and methods to analyze data and solve problems across fields including government, healthcare, and scientific research. The role tends to be more theory-focused than data scientist positions, often involving the design of studies and surveys in addition to analyzing the resulting data.

The Bureau of Labor Statistics groups statisticians together with mathematicians in a combined occupational category, which carries a median annual wage of $105,720 and a projected growth rate of 10% through 2035, with about 2,000 openings projected each year across both occupations. Many statistician positions call for a master's degree, so data science graduates interested in this path often continue on to graduate study in statistics or a closely related field.

7. Business Intelligence Analyst

Business intelligence analysts build the dashboards and reports that help organizations track performance and spot trends, translating raw data into a format business leaders can act on quickly. The role sits closer to the business side of data work, requiring strong communication skills alongside technical ability with tools like SQL and data visualization software.

Business intelligence analyst isn't tracked as its own BLS occupation, so the closest classification is Management Analysts, which carries a median annual wage of $101,860 and a projected growth rate of 10% through 2035, with about 94,100 openings projected each year. Data science graduates bring strong technical chops to this role, and those who enjoy the business-facing side of data work often find it a natural fit.

8. Operations Research Analyst

Complex logistical and strategic problems, from optimizing supply chains to improving healthcare delivery systems, are where operations research analysts specialize, applying mathematical modeling and advanced analytics to find the best solution. The work often involves building models that simulate different scenarios, then recommending the option that best balances cost, efficiency, and other organizational priorities.

This role carries a median annual wage of $88,940, with employment projected to grow 12% through 2035 and about 7,500 openings projected each year. The heavy emphasis on mathematical modeling makes this a strong fit for data science graduates who enjoy applying quantitative methods to real-world business problems.

9. Data Analyst

Data analysts work with existing data to identify trends, build reports, and answer specific business questions, making this one of the most common entry points into a data career. The role typically involves less programming and machine learning than a full data scientist position, leaning instead on tools like SQL, spreadsheets, and business intelligence dashboards.

According to Indeed, data analysts earn an average salary of $86,686 per year, with typical pay ranging from $53,773 to $139,744 depending on experience and location; entry-level analysts average closer to $21.24 an hour, while senior data analysts average $106,531 annually. The Bureau of Labor Statistics doesn't track data analyst as a separate occupation, generally folding this kind of work into its broader data scientist classification, which is worth keeping in mind since that category's median wage runs well above typical data analyst pay.

10. Market Research Analyst

Consumer preferences, market conditions, and competitor behavior make up the bread and butter of a market research analyst's work, feeding into decisions companies make about products, pricing, and marketing strategy. The role blends data analysis with an understanding of business strategy, often involving survey design and consumer behavior research alongside more traditional data work.

Market research analysts earn a median annual wage of $78,760, with employment projected to grow 7% through 2035 and about 82,000 openings projected each year, the largest number of annual openings of any career on this list. A data science background provides strong analytical skills for this role, and the position offers a path for graduates interested in applying data skills specifically to marketing and consumer behavior.

How We Chose These Careers

We selected these 10 careers based on a few practical factors that matter most to someone deciding where to point a data science degree. Here's what we looked at.

• Salary potential: Each career offers pay well above the median wage for all occupations, reflecting the strong market value of data and analytical skills.
• Job growth: Every role on this list is projected to grow at least as fast as the average for all occupations, and most grow considerably faster.
• Relevance to data science skills: Each career draws directly on the statistics, programming, or analytical thinking a data science curriculum is built to teach.

Skills From a Data Science Degree That Apply Across These Careers

A handful of core skills show up across nearly every career on this list, even though the day-to-day work looks quite different from role to role. Building strength in these areas can open doors across the full range of careers above rather than locking you into just one path.

• Statistical analysis and probability
• Programming in Python, R, or SQL
• Data cleaning and preparation
• Data visualization and reporting
• Machine learning fundamentals
• Communicating technical findings to nontechnical audiences

Which Career Path Fits You Best?

The careers on this list draw on different strengths, so it helps to think about which type of work energizes you most before choosing a direction. Here's a rough breakdown to help narrow things down.

If You Like Building and Coding

Machine learning engineer and data engineer roles lean heavily on software engineering skills, so they suit graduates who enjoy writing code and building systems as much as analyzing data. These paths often require deepening your programming skills beyond what a typical data science curriculum covers.

If You Like Numbers and Theory

Statistician, actuary, and operations research analyst roles reward a strong theoretical grounding in math and statistics, along with comfort building and interpreting complex models. These paths often lead toward graduate study or professional certification exams on top of an undergraduate degree.

If You Like Business and Strategy

Business intelligence analyst, market research analyst, and data analyst roles sit closer to the business side of data work, requiring strong communication skills alongside technical ability. These positions suit graduates who enjoy translating data into recommendations that shape real business decisions.

Education Needed for These Careers

Most careers on this list require at least a bachelor's degree in data science, statistics, computer science, or a closely related quantitative field. That bachelor's degree covers the core skills nearly every role on this list draws on, including statistics, programming, and data analysis, even though the specific tools and techniques emphasized vary by career.

Several roles, including computer and information research scientist and statistician, typically call for a master's or doctoral degree, particularly for research-focused or highly specialized positions. Actuaries follow a somewhat different path, since a bachelor's degree provides the quantitative foundation but professional advancement in that field depends heavily on passing a series of actuarial exams rather than pursuing additional formal education.

FAQs About Data Science Career Paths

Here are answers to some common questions people have when weighing which data science career path to pursue. These cover practical considerations that go beyond the career profiles above.

Can I Switch Between These Careers Later?

Yes, and it happens quite often, since the underlying skills across these careers overlap significantly. Many professionals start as data analysts or data scientists and move into more specialized roles like machine learning engineer or operations research analyst as they build additional expertise.

How Long Does It Take to Complete a Data Science Degree?

A bachelor's degree in data science typically takes about four years of full-time study, similar to most other bachelor's programs. Graduate degrees, which some careers on this list require or prefer, generally add another one to two years of study on top of that foundation.

Are These Careers Available in Remote Roles?

Many of them are, particularly data scientist, data analyst, machine learning engineer, and business intelligence analyst positions, since the work often happens primarily on a computer. Roles like actuary and operations research analyst sometimes require more in-person collaboration, though remote and hybrid arrangements have become increasingly common across the field.

What Certifications Complement a Data Science Degree?

Data science certifications in specific tools and platforms, such as cloud computing certifications or specialized machine learning credentials, can strengthen a data science degree for particular career paths. For actuaries specifically, passing professional actuarial exams matters far more than any supplemental certificate, since those exams are a required part of career advancement in that field.

Which of These Careers Has the Fastest Job Growth?

Data scientist roles lead the pack with 35% projected growth through 2035, well ahead of every other career on this list. Operations research analyst and computer and information research scientist roles also show strong growth, at 12% and 22% respectively, making all three solid choices for graduates prioritizing long-term job security.

Explore Data Science Degree Programs

Browse data science degrees through Learn.org and connect directly with schools to learn more about program formats, admission requirements, and how each one prepares you for these career paths. Comparing programs side by side is the best way to find the right fit for your career goals and budget.