DATA SCIENCE
CAREERS

A Master of Applied Data Science opens the door to several distinct career paths, each with its own day-to-day focus and pay range. Here is a closer look at seven common roles graduates pursue.

Data scientists collect, clean and analyze large datasets to build the predictive models that turn raw information into forecasts and recommendations organizations can act on. The role leans on Python or R, SQL and machine learning frameworks such as scikit-learn or TensorFlow.

According to the U.S. Bureau of Labor Statistics (BLS), the median annual wage for data scientists was $120,230 as of May 2025, with employment projected to grow 35% through 2035.

Machine learning engineers build models and turn them into the production systems that power product recommendations and real time fraud detection. The work draws on software engineering fundamentals, cloud platforms like AWS or Azure, and deep learning frameworks such as TensorFlow or PyTorch to keep those systems fast and reliable at scale.

BLS groups this work closest to computer and information research scientists, who earned a median annual wage of $140,300 as of May 2025, with growth projected at 22% from 2025 to 2035.

Marketing analysts track campaign performance, customer behavior and market trends to help teams refine who they target and how. Day to day work involves tools like Google Analytics, marketing automation platforms such as HubSpot, and A/B testing to measure what is actually working.

BLS reports a median annual wage of $78,760 for market research analysts and marketing specialists, the closest match, with 7% growth projected through 2035.

AI specialists design and train the algorithms behind tools like chatbots and generative AI applications, systems built to interpret language, images, or other unstructured input and respond in real time. The work requires strong Python skills, experience with neural network architectures, and familiarity with frameworks like PyTorch or Hugging Face, the libraries used to build and fine tune those models.

BLS doesn’t track this title separately, so the closest wage benchmark is computer and information research scientists, at $140,300 annually in 2025, with growth projected at 22% through 2035.

Supply chain specialists coordinate the movement of goods and materials, forecasting demand and managing the flow between suppliers, warehouses, and customers. The role increasingly relies on ERP systems, demand planning software, and SQL or Excel based modeling to keep that flow efficient.

BLS reports logisticians, the closest matching occupation, earned a median annual wage of $82,320 in 2025, with 18% growth projected through 2035.

Data analysts clean, organize and interpret datasets to answer specific business questions, most often through dashboards and reports rather than predictive models. The role is built around SQL, spreadsheet tools, and visualization platforms like Tableau or Power BI, software that turns raw numbers into charts and dashboards non-technical teams can read at a glance.

BLS groups this work closest to operations research analysts, who earned a median annual wage of $88,940 in 2025, with 12% growth projected through 2035.

Data consultants step into an organization to audit its data infrastructure and recommend how it should be collected, stored and used, often moving from client to client rather than staying on one team long term. The work draws on SQL and data visualization tools like Tableau or Power BI, along with the project management skills needed to guide a client through implementing new systems.

BLS groups this work closest to management analysts, who earned a median annual wage of $101,860 in 2025, with 10% growth projected through 2035.

Every industry now runs on data, and organizations need people who can turn it into decisions that matter. Professionals with a Master of Applied Data Science are positioned to pursue that work across nearly any sector, from health care and finance to government and retail.

In tech, data scientists optimize the algorithms and user experiences that power the apps, websites, and platforms people use every day.

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Public and private school systems rely on data scientists to track learning outcomes and anticipate shifts in student populations.

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Local, state and federal agencies use data scientists to analyze public policy, understand resident needs and track demographic change.

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Health care data scientists combine patient information and clinical research to improve outcomes and support medical decision making.

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Financial services firms rely on data scientists to detect fraud, manage risk and improve how markets are analyzed.

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Energy companies use data scientists to keep equipment running, understand environmental impact and manage complex supply chains.

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Retail and marketing teams use data scientists to understand purchasing behavior and personalize the customer experience.

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Cybersecurity teams rely on data scientists to detect malicious activity and strengthen systems against future attacks.

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In sports and entertainment, data scientists track audiences, personalize content and analyze performance on and off the field.

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Students in the online Master of Applied Data Science (MADS) get academic advising, career guidance, and tech support throughout the program, plus faculty mentors who come from UNC’s Departments of Computer Science, Mathematics, and Statistics and Operations Research.

The program offers 1:1 career coaching sessions and mock interviews so students graduate ready to hit the job market.

The centerpiece of that mentorship is a team-based capstone, where faculty and staff draw on their own industry, government and community connections to place students on a real data problem for an actual organization. That project becomes a portfolio piece built on real stakeholder work, the kind of experience employers actually look for. Students can also opt into a non-credit immersion on campus in Chapel Hill to build in-person connections with classmates and faculty before entering the job market.

When evaluating a Master of Applied Data Science (MADS) vs. a traditional Master of Science in Data Science (MSDS), the distinction comes down to practical workplace execution versus theoretical research.

A traditional, thesis-driven data science degree serves a different audience, typically preparing students for a PhD or a research career rather than industry work. For someone looking to move into a data science role quickly, or advance in one they already hold, that practitioner focus offers the more direct path.

Ready to see what a career in data science could look like for you? Request information about UNC’s online Master of Applied Data Science and talk with an enrollment specialist about your next step.

• Is data science still a good career in 2026?

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  Yes. Data science puts you at the center of interesting problems, from helping hospitals anticipate patient needs to helping energy companies keep equipment running. Every industry generates data, so you can choose a field you care about and apply your skills there. The work also leaves room for creativity, since building a model or designing an analysis means deciding how to approach a question nobody has answered yet, and those answers often shape real products, policies, and services.

• What is the difference between a data scientist and a data analyst?

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  Data analysts focus on interpreting existing data to answer specific business questions, typically through dashboards and reports. Data scientists go a step further, building predictive models that forecast what is likely to happen next. UNC’s guide on data science versus data analytics breaks down these distinctions in more depth.

• Can I switch into data science from a different career?

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  Yes. The technical and theoretical knowledge you build in a program like MADS sits on top of the experience you already have, and that combination is valuable. A former teacher understands how learning outcomes get measured, and a nurse knows how clinical data gets created in the first place. Domain knowledge like that helps you ask better questions and explain your findings to the people who will act on them. Your previous career gives you a perspective that sets your analysis apart.

• How long does it take to complete UNC’s online Master of Applied Data Science?

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  The online Master of Applied Data Science can be completed in as few as 16 months on the accelerated track, taking three courses per term, or over roughly two years on the standard track, taking two courses per term. Students can start in either January or August.

Created by the online Master of Applied Data Science (MADS) program from the University of North Carolina at Chapel Hill.