Artificial intelligence is reshaping every industry, from healthcare and finance to manufacturing and entertainment. The technology that once seemed futuristic now powers the tools we use daily, and the professionals who build, refine, and deploy these systems are in unprecedented demand.

If you're considering a career in AI, you're looking at one of the most dynamic and rewarding fields in technology. AI professionals command competitive salaries, work on cutting-edge innovations, and have the opportunity to solve real-world problems that impact millions of lives.

If you're in the New York metro area, you're positioned in one of the world's leading tech hubs, where AI talent is highly sought after and generously compensated.

This guide explores the careers in artificial intelligence that are shaping the future, the skills you'll need to break into the industry, and how Pace University's AI programs can prepare you to thrive in this fast-growing field.

What Are Careers in Artificial Intelligence?

Careers in AI revolve around building, developing, and maintaining the intelligent systems that perform tasks traditionally requiring human cognition, such as learning, reasoning, problem-solving, and decision-making. Today, AI powers applications and analytical tools used across virtually every industry.

In healthcare, AI-powered diagnostic tools analyze medical images, predict patient outcomes, and streamline administrative workflows, allowing physicians to focus on patient care. In finance, AI automates fraud detection, processes credit scoring, and powers chatbots that handle customer inquiries 24/7. Government agencies use AI to increase operational efficiency, support data-driven decision-making, and analyze vast datasets that would be impossible to review manually.

This widespread adoption has created a growing need for professionals who understand core AI concepts, including the following:

• Machine learning: Algorithms that enable machines to learn from data and improve performance over time without explicit programming.
• Deep learning: Advanced neural networks that process complex patterns in data, powering applications like image recognition and natural language understanding.
• Natural language processing (NLP): Tools that allow machines to understand, interpret, and generate human language for applications like chatbots and translation services.
• Computer vision: Systems that interpret visual data like images and videos, enabling facial recognition, autonomous vehicles, and medical imaging analysis.
• Cloud computing and AI deployment: Scalable infrastructure to store massive datasets, manage computing power, and deploy AI solutions efficiently.
• Big data management: The ability to collect, process, and analyze enormous datasets that fuel AI model training and insights.

Professionals in AI careers might work as engineers building machine learning models, researchers pushing the boundaries of what's possible with AI, ethicists ensuring responsible AI development, or product managers translating technical capabilities into business solutions.

Which Skills Are Essential for AI Jobs?

Breaking into AI requires a blend of technical expertise and soft skills that allow you to apply complex technologies in practical, impactful ways.

Technical Skills

To build and deploy AI systems effectively, you'll need expertise in:

• Programming: You should understand and have hands-on experience in at least one of the following programming languages: Python (the most widely used in AI), R (valuable for statistical analysis), C/C++ (for performance-critical applications), or Java (common in enterprise environments).
• Database and data management: Develop your expertise in SQL and database management systems to query and manipulate the large datasets that power AI models. Understand data warehousing concepts and tools that store and organize information efficiently.
• Data visualization: Enhance your ability to communicate insights using visualization tools like Tableau or Power BI, which help translate complex data findings into actionable business intelligence.
• Mathematics and statistics: Build a strong foundation in linear algebra, calculus, probability, and statistics. These underpin the algorithms and models at the heart of AI systems.
• AI frameworks and tools: Gain experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, which streamline the development and deployment of AI models.

Soft Skills

Technical knowledge alone won't set you apart. AI professionals must also excel at:

• Research and analytical thinking: The ability to investigate problems, test hypotheses, and derive insights from data.
• Collaboration: AI projects typically involve cross-functional teams including engineers, designers, business stakeholders, and domain experts.
• Communication: Translating complex technical concepts into language that non-technical stakeholders can understand and act upon.
• Critical thinking and problem-solving: Approaching challenges methodically, identifying root causes, and developing innovative solutions.
• Ethical reasoning: Understanding the societal implications of AI systems and making decisions that prioritize fairness, transparency, and accountability.

Pace University's AI programs equip students with these essential skills through hands-on learning, access to high-tech facilities, collaboration with innovative faculty conducting cutting-edge research, and a curriculum grounded in ethical frameworks. With three AI degree options and over 40 courses, Pace prepares you to enter the field with confidence.

AI Demand and Job Outlook

The career outlook for AI professionals is exceptionally strong. While the U.S. Bureau of Labor Statistics doesn't track "AI jobs" as a distinct occupational category, AI professionals typically fall under related occupations with robust growth projections.

Computer and Information Research Scientists—a category that includes AI research scientists, algorithm developers, and research-focused roles—is projected to grow 20% from 2024 to 2034, much faster than the average for all occupations. The Data Scientists occupational category, which includes many AI specializations and machine learning applications, shows even stronger momentum with a projected growth rate of 34% over the same period, making it one of the fastest-growing occupations in the technology sector.

Beyond the numbers, market dynamics tell a compelling story. The World Economic Forum reports that advancements in AI and information processing technologies (86%) and robotics and automation (58%) are the primary drivers of demand for technology-related skills. AI and big data, networks and cybersecurity, and technological literacy are anticipated to be the three fastest-growing skill areas globally.

Careers such as Big Data Specialists, FinTech Engineers, AI and Machine Learning Specialists, and Software and Application Developers are among the fastest-growing jobs, reflecting how AI capabilities are becoming mission-critical across industries.

For professionals in the New York metro area, the opportunity is even greater. New York City has emerged as a global AI hub, home to major tech companies, innovative startups, world-class research institutions, and financial services firms investing heavily in AI transformation. This concentration of AI activity translates into higher salaries, diverse career pathways, and unparalleled networking opportunities.

Top Careers in AI

The following 15 careers represent some of the most sought-after roles in artificial intelligence. Each offers unique challenges, opportunities for impact, and strong earning potential—especially in the New York metro area.

1. AI Engineer

AI engineers design, develop, and deploy machine learning models that solve real-world problems. They build the infrastructure that allows AI systems to function in production environments, working closely with data scientists and software developers to bring AI solutions to life. AI engineers need strong programming skills, expertise in machine learning frameworks, and an understanding of cloud platforms for scalable deployment.

Key Skills: Python, TensorFlow, PyTorch, cloud platforms (AWS, Azure, Google Cloud), model optimization, MLOps

NY Metro Salary Range: $136,000–$228,000

2. AI Ethicist

As AI systems become more prevalent, the need for professionals who ensure these technologies are developed and used responsibly has never been greater. AI ethicists address critical concerns around bias, fairness, transparency, privacy, and accountability. They work with development teams to identify potential ethical issues, develop governance frameworks, and ensure AI practices align with legal requirements and societal values.

Key Skills: Ethics frameworks, regulatory knowledge, risk assessment, stakeholder communication, policy development

NY Metro Salary Range: $153,000–$248,000

3. AI Product Manager

AI product managers bridge the gap between technical teams and business stakeholders, guiding the development of AI-powered products from concept to launch. They define product vision, prioritize features, manage roadmaps, and ensure that technical capabilities translate into user value and business outcomes. This role requires both technical fluency and strategic thinking.

Key Skills: Product strategy, roadmap management, stakeholder communication, technical understanding of AI/ML, user experience design, agile methodologies

NY Metro Salary Range: $174,000–$263,000

4. AI Solutions Architect

AI solutions architects design the technical architecture for AI systems, ensuring they're scalable, efficient, and integrated seamlessly with existing infrastructure. They make high-level decisions about technology stacks, data pipelines, model deployment strategies, and system integration. This role requires deep technical expertise combined with a strategic understanding of business requirements.

Key Skills: System design, cloud architecture, data pipeline design, machine learning infrastructure, technical leadership

NY Metro Salary Range: $224,000–$359,000

5. Big Data Specialist

Big data specialists manage and analyze massive datasets that fuel AI applications. They design data architectures, implement data processing pipelines, and ensure data quality and accessibility. These professionals work with tools like Hadoop, Apache Spark, and NoSQL databases to handle data at scale.

Key Skills: Hadoop, Apache Spark, data warehousing, distributed computing, ETL processes, data governance

NY Metro Salary Range: $89,000–$160,000

6. Business Intelligence Developer

Business intelligence developers leverage AI and machine learning to extract actionable insights from data and support strategic decision-making. They design dashboards, automate reporting processes, and build predictive analytics tools that help organizations understand trends and optimize operations.

Key Skills: SQL, Power BI, Tableau, data modeling, statistical analysis, business acumen

NY Metro Salary Range: $111,000–$174,000

7. Data Analyst

Data analysts collect, process, and analyze data to uncover patterns and insights that inform business decisions. In AI-driven organizations, data analysts often work closely with data scientists and machine learning engineers to prepare datasets, validate model outputs, and communicate findings to stakeholders.

Key Skills: SQL, Excel, Python or R, data visualization, statistical analysis, critical thinking

NY Metro Salary Range: $74,000–$126,000

8. Data Scientist

Data scientists use statistical methods and machine learning techniques to extract insights from complex datasets. They build predictive models, conduct experiments, and translate data findings into strategic recommendations. This role sits at the intersection of statistics, programming, and business strategy.

Key Skills: Python or R, machine learning algorithms, statistical modeling, data visualization, experimentation, domain expertise

NY Metro Salary Range: $128,000–$217,000

9. FinTech Engineer

FinTech engineers develop AI solutions specifically for financial technology applications. They build systems for fraud detection, algorithmic trading, credit scoring, risk assessment, and personalized financial planning. This role requires both technical AI expertise and a deep understanding of financial systems and regulations.

Key Skills: Machine learning, Python or Java, financial domain knowledge, regulatory compliance, real-time systems

NY Metro Salary Range: $150,000–$279,000

10. Research Scientist

AI research scientists push the boundaries of what's possible in artificial intelligence. They conduct experiments, develop novel algorithms, publish research papers, and collaborate with academic and industry partners to advance the field. This role typically requires an advanced degree and a strong publication record.

Key Skills: Advanced mathematics, deep learning, research methodologies, scientific writing, algorithm development

NY Metro Salary Range: $182,000–$295,000

11. Machine Learning Engineer

Machine learning engineers design, build, and deploy ML models that power intelligent applications. They work on recommendation systems, predictive analytics, natural language processing applications, and computer vision solutions. This role emphasizes production-ready code, model optimization, and system scalability.

Key Skills: Python, TensorFlow or PyTorch, feature engineering, model deployment, software engineering best practices

NY Metro Salary Range: $137,000–$220,000

12. Natural Language Processing Engineer

NLP engineers develop systems that understand, interpret, and generate human language. They build chatbots, translation services, sentiment analysis tools, text summarization systems, and voice assistants. This specialized role requires expertise in linguistics, machine learning, and language models.

Key Skills: NLP libraries (spaCy, NLTK, Hugging Face), transformers, language modeling, Python, linguistic knowledge

NY Metro Salary Range: $131,000–$194,000

13. Robotics Engineer

Robotics engineers design, build, and program robots that perform tasks autonomously or semi-autonomously. They integrate AI technologies to enhance robot functionality, enabling applications in manufacturing, healthcare, logistics, and exploration. This role combines mechanical engineering, electrical engineering, and AI.

Key Skills: Robotics programming (ROS), sensor integration, machine learning, control systems, mechanical design

NY Metro Salary Range: $110,000–$185,000

14. Software/Application Developer

Software developers in AI-focused roles build applications that integrate machine learning capabilities. They create user-facing products, APIs, and systems that leverage AI to deliver intelligent functionality. This role emphasizes software engineering fundamentals alongside AI integration skills.

Key Skills: Python, Java, or C++, software architecture, API design, version control, testing and debugging

NY Metro Salary Range: $122,000–$183,000

15. UX Designer/Developer

UX designers and developers in AI create intuitive interfaces for AI-powered applications. They ensure users can interact effectively with intelligent systems, designing experiences that are accessible, transparent, and aligned with user needs. This role blends design thinking with technical implementation.

Key Skills: UI/UX design, front-end development (HTML, CSS, JavaScript), user research, prototyping tools (Figma, Sketch), accessibility standards

NY Metro Salary Range: $100,000–$162,000

Salary Disclaimer: Salary figures sourced from Glassdoor in February 2026 for the New York, NY, metro area. Actual salaries can vary based on factors including geographic location, specific employer, years of experience, education level, and available salary submission data. These ranges represent typical compensation and should be used as general guidelines.

How to Pursue a Career in AI

Pursuing a career in artificial intelligence requires a combination of foundational knowledge, hands-on experience, and strategic career planning. Here's how to position yourself for success.

Step 1. Build Your Foundation

• Gain a basic understanding of AI and math fundamentals: Start by exploring the basics of AI through articles, videos, and introductory courses. Learn the difference between supervised and unsupervised learning, understand how neural networks function, and explore deep learning concepts. Strengthen your foundation in linear algebra, calculus, probability, and statistics. These mathematical concepts underpin AI algorithms.
• Learn programming languages: Focus on languages commonly used in AI, particularly Python (the dominant language in the field), R (valuable for statistical analysis and data science), and Java (used in enterprise environments). Build comfort with data structures, algorithms, and object-oriented programming.
• Practice with AI tools: Get hands-on experience by exploring open-source AI projects on platforms like GitHub, GitLab, or Bitbucket. Work through tutorials, replicate existing models, and experiment with datasets. Participate in Kaggle competitions to test your skills against real-world problems and learn from the community.

Step 2. Invest in Education

• Consider a formal education: While self-learning is valuable, a structured academic program provides comprehensive training, access to expert faculty, hands-on projects, and credentialing that employers recognize. A strong academic background in computer science, mathematics, data science, or a related field can significantly accelerate your career trajectory. Many universities now offer specialized degree programs in AI and machine learning that provide both theoretical foundations and practical applications.
• For those looking to differentiate themselves in the job market, a master's degree in artificial intelligence offers advanced training, research opportunities, and deeper specialization. Graduate programs prepare you for leadership roles and complex technical challenges.

Step 3. Build Experience and Network

• Join AI communities: Engage with online communities of AI enthusiasts and professionals on platforms like LinkedIn, Reddit, GitHub, and specialized forums. Attend meetups, conferences, and workshops where you can learn from practitioners, discover the latest developments, and build relationships that can lead to opportunities.
• Seek internships and projects: Apply for internships at companies working on AI, participate in research projects at universities, or contribute to open-source AI initiatives. These experiences provide real-world context, strengthen your resume, and help you build a portfolio that demonstrates your capabilities.
• Stay current: AI is a rapidly evolving field. Follow leading researchers, read papers, take online courses on emerging techniques, and continuously update your skills to remain competitive.

Majoring in AI at Pace University

Pace University offers three comprehensive AI degree programs designed to prepare you for success in today's AI-driven world. Whether you're just starting your undergraduate journey or looking to advance your career with graduate education, Pace provides the hands-on experience, technical expertise, and ethical grounding you need to thrive.

The Bachelor of Science in Artificial Intelligence builds a strong foundation in AI concepts, programming, mathematics, and real-world applications. You'll gain expertise in machine learning, data science, and AI development while exploring ethical considerations and societal impacts.

The Master of Science in Artificial Intelligence is an advanced degree that dives deep into cutting-edge AI technologies, research methodologies, and specialized applications. You'll work on complex projects, collaborate with faculty on research initiatives, and position yourself for leadership roles in the field.

The Master of Science in Applied Artificial Intelligence is designed for professionals looking to apply AI in real-world contexts, this program emphasizes practical skills, industry applications, and the translation of AI capabilities into business value.

What sets Pace apart? Location is a major advantage—studying in the New York metro area means access to one of the world's leading tech hubs. You'll find internships, networking events, and career opportunities at major corporations, innovative startups, and research institutions all within reach of Pace's campuses.

Pace faculty are at the forefront of AI-related research across diverse applications, from AI ethics and governance to social media, pedagogy, and healthcare. Recent faculty-student collaborations have explored AI applications in mental health diagnostics through social media analysis, machine learning for speech disorder diagnosis, and ethical monitoring systems for AI behavior. Students often collaborate with faculty on cutting-edge projects, contributing to research that advances the field.

The Pace Artificial Intelligence Lab—housed in the cutting-edge 15 Beekman building in NYC—combines research, education, partnerships, and training to drive critical advances in AI innovation. With over 30 years of AI research and development expertise, the lab serves as the central AI hub for the Pace community, providing access to the tools, technology, and mentorship used by leading AI professionals.

Pace's Augmented Intelligence Lab focuses on the human side of that equation, exploring how people and AI systems collaborate, make decisions, and build trust. Students in the lab work with faculty on research in areas such as healthcare applications, cybersecurity decision support, and human-centered AI design.

With over 40 AI-related courses taught by 14+ faculty actively researching in the field, Pace offers depth and breadth of learning. Every Pace student begins their AI journey with CIS 101, which introduces AI fundamentals and ethical use, ensuring all graduates enter the workforce with AI literacy.

Learn more about your career opportunities in AI at Pace University.

FAQs

How do I start a career in AI?

Start by learning Python and building foundational skills in mathematics and statistics. Pursue a bachelor's degree in computer science, data science, or AI. Build a portfolio through personal projects and Kaggle competitions, then apply for internships. Consider a master's degree to access advanced roles and higher salaries.

How can I get an AI job with no experience?

Begin with entry-level roles like junior data analyst or research assistant. Build a portfolio of AI projects on GitHub, take online courses, and earn certifications from platforms like Coursera. Participate in Kaggle competitions and network at AI meetups to make connections in the field.

Is AI a high-paid job?

Yes, AI careers tend to earn high salaries. In New York metro, entry-level positions start around $74,000 to $100,000, mid-level roles earn $128,000 to $220,000, and senior positions reach $224,000 to $359,000. Tech hubs including NYC offer significantly higher compensation than national averages.

Which industries use AI?

AI is used across technology, healthcare, financial services, manufacturing, retail, government, transportation, education, agriculture, energy, and entertainment industries.

What skills do I need to work in AI?

You need Python proficiency, understanding of machine learning algorithms, and experience with frameworks like TensorFlow or PyTorch. Essential knowledge includes SQL, cloud platforms, and mathematics (linear algebra, calculus, statistics). Important soft skills are analytical thinking, problem-solving, communication, and collaboration.

Step Into the Future of AI at Pace University

Ready to launch your AI career? Explore Pace University's AI programs and discover how we prepare students to lead in this transformative field.

You're scrolling through job postings and the requirements start to blur together. One company wants a “data scientist with machine learning expertise.” Another is hiring an “AI engineer with strong analytics skills.” A third asks for a “machine learning engineer who can tell stories with data.”

Aren’t these different jobs? Yes and no. Data science and artificial intelligence are distinct disciplines with different goals and day-to-day responsibilities, yet they often overlap, which can make choosing a degree less straightforward.

Should you pursue data science or artificial intelligence? And does that decision lock you into a single track?

It doesn’t. Your degree shapes your early direction, but it does not define your long-term career. Understanding how these fields differ, where they intersect, and what kind of work you enjoy will help you choose your path with confidence.

The Core Difference (That Everyone Gets Wrong)

Ask someone to explain the difference between data science and AI, and you’ll often hear: “AI is advanced data science” or “Data science comes before AI.” That framing misses the real distinction.

• Data science starts with data and asks, “What does this tell us?”
• Artificial intelligence starts with a goal and asks, “How can we build a system that learns and acts?”

Data scientists work from information to insight. They analyze customer behavior, sales trends, or operational data to explain what happened and predict what might happen next. Their focus is interpretation and decision support.

AI engineers work from inputs to action. They design systems that recognize patterns, generate responses, or make real-time decisions without constant human oversight. Their focus is building capability.

Consider a lending example: A data scientist studies past loan applications to identify patterns behind default risk. An AI engineer builds a system that evaluates new applications automatically. One role focuses on explanation. The other focuses on construction.

If you’re energized by asking questions, testing ideas, and translating findings for business leaders, data science may fit your strengths. If you prefer designing systems, optimizing performance, and solving technical engineering problems, AI may align more closely with your interests.

Of course, this distinction has blurred in recent years. Generative AI tools and large-scale machine learning systems have pushed these fields closer together. Data scientists build models, and AI engineers analyze data. Many roles now require fluency in both.

Data Science vs. AI: A Side-by-Side Comparison

Data science and artificial intelligence share technical foundations, but they diverge in approach, workflow, and career outcomes. If you’re choosing a degree, those differences influence what you study and the roles you pursue after graduation.

Focus and Purpose

At a high level, the distinction comes down to intent. Data science centers on interpreting data to guide decisions, while AI centers on building systems that learn from data and act independently.

Daily Work and Mindset

The contrast becomes more practical when you look at daily responsibilities. The rhythm of the work, the types of problems tackled, and the teams involved often differ significantly.

Core Skills and Tools

Both fields require strong programming and quantitative foundations, but the emphasis shifts. Data science leans heavily on statistics and communication, since insights must influence business decisions. AI leans further into algorithm design, model architecture, and software engineering, since systems must operate reliably at scale.

Output and Deliverables

Career outcomes reflect these differences. Data science roles typically produce analysis that guides human decision-making. AI roles result in applications or systems that automate parts of that decision-making process.

Where They Overlap

The separation is not absolute. Both fields rely on shared mathematical foundations and machine learning techniques, and many professionals develop fluency in both over time.

Career Paths: Where Each Degree Takes You

In the New York metro area, employers across finance, healthcare, media, and tech actively hire both data scientists and AI engineers. While earning potential is strong in both fields, the early career trajectory and daily responsibilities can differ depending on your degree.

Data Science Career Track

In data science, progression often moves from analysis and support work toward strategy and leadership.

Within data science, common specializations include:

• Data Engineering: Building and maintaining data pipelines, databases, and infrastructure
• Business Intelligence: Creating dashboards, defining metrics, and translating data into accessible reporting
• Quantitative Analysis: Applying statistical modeling in finance and investment environments

AI Career Track

AI roles typically emphasize engineering depth earlier and scale into technical leadership or research.

Common AI specializations include:

• Computer Vision: Building systems that interpret images and video
• Natural Language Processing (NLP): Designing systems that understand and generate human language
• Robotics: Integrating AI with hardware for autonomous systems
• AI Ethics and Governance: Ensuring fairness, transparency, and responsible system behavior

Roles That Combine Both

Some careers draw equally from data science and AI expertise. These roles reward professionals who understand analytics and engineering.

Salary Information Disclaimer: Salary data was sourced from Glassdoor in February 2026. Actual compensation may vary based on location, experience level, employer, industry, and reporting methodology.

Educational Pathways: Choosing Your Degree

Your degree should align with the type of work you want to do and the level of technical depth you want to build. At Pace University’s Seidenberg School of Computer Science and Information Systems, students can pursue AI and data science through programs designed for both foundational learning and advanced specialization.

Undergraduate Level: Building Your Foundation

The BS in Artificial Intelligence at Pace prepares students to become AI specialists with a strong foundation in computer science and mathematics. The curriculum begins with computing fundamentals and object-oriented programming, then advances into machine learning, neural networks, computer vision, and AI ethics.

Students gain hands-on experience through:

• Research in the Pace AI Lab
• Faculty-mentored projects
• Workshops, hackathons, and industry panels
• Access to more than 40 AI-related courses across the curriculum

With more than 30 years of AI teaching and research experience, Seidenberg integrates AI throughout the student experience, beginning with first-year coursework that introduces ethical AI use.

Located in New York City, the program provides direct access to internships and networking opportunities in one of the country’s most active tech markets.

Master's Level: Specialization and Career Advancement

At the graduate level, Pace offers three distinct pathways depending on your career goals.

Data Science, MS

The MS in Data Science focuses on turning large data sets into actionable insight. Students work with live data in advanced labs, including the Computational Intelligence Lab, and develop skills in statistical modeling, machine learning, big data technologies, and data storytelling.

The program is STEM-designated and offered in person and online. Bridge courses are available for students without prior data science experience, making it accessible to career changers while maintaining technical rigor.

Graduates are prepared for roles such as data scientist, analytics specialist, and advanced data analyst across industries including finance, healthcare, and media.

Artificial Intelligence, MS

The MS in Artificial Intelligence emphasizes advanced technical depth and research experience. Students study machine learning, natural language processing, robotics, generative AI, and computer vision while collaborating on faculty-led research projects.

Seidenberg faculty conduct AI research across healthcare, education, robotics, and human-centered AI. Students have access to:

• The Pace AI Lab
• The Robotics Lab
• The Augmented Intelligence Lab
• Faculty working on NIH-funded and interdisciplinary research

This program prepares graduates for engineering-intensive and research-oriented AI roles.

Applied Artificial Intelligence, MS

The MS in Applied Artificial Intelligence bridges theory and real-world deployment. The curriculum is project-driven and designed for professionals who want to implement AI solutions in business environments.

Students learn to:

• Train, evaluate, and deploy machine learning systems
• Integrate large language models into production workflows
• Design human-centered AI systems
• Address ethical and governance challenges

With concentrations in human-centric AI, data-centric AI, and computational intelligence, the program allows students to tailor their technical focus while building applied experience.

A Distinct Advantage: AI Across the University

AI is embedded throughout the University, with three dedicated AI degrees, more than 40 AI-related courses, and more than 14 faculty conducting active research.

From healthcare research to ethical AI frameworks, Seidenberg students participate in projects that extend beyond the classroom and into real-world applications.

Pace Students In Action

Across both tracks, Pace students gain hands-on experience through research projects, industry partnerships, and immersive programs. Here are a few recent examples:

When IBM partnered with the Pace Data Science Society for a full-day hackathon, student teams used Watson Orchestrate to prototype AI workflow solutions for real university use cases, with support from over 15 IBM mentors.

The AI Internship Experience, a two-week immersive summer program led by Seidenberg faculty, introduced 18 students to machine learning, deep learning, and multimodal generative AI through hands-on projects and industry networking.

Stephanie Sicilian began her Pace journey as a biology major and volleyball player before discovering her passion for technology. Now pursuing her MS in Information Systems with a data science concentration, she co-leads research projects and helped organize an AI tools workshop through the Pace AI Lab.

Darsh Joshi, a data science graduate student, has used Seidenberg’s Computational Intelligence Lab to power machine learning research, from teaching machines to recognize hand gestures to exploring machine unlearning. He plans to launch a startup after graduation.

These experiences reflect the hands-on, project-driven approach that defines a Seidenberg education, and they're available to students at every stage of their academic journey.

Career Changer Considerations

Professionals transitioning into AI or data science benefit from structured, hands-on programs that build both portfolio work and technical depth.

Pace supports working professionals through STEM-designated graduate programs, career services, and the INSPIRE and ASPIRE career readiness initiatives. With campuses in New York City and Westchester, students gain proximity to companies actively hiring in both AI and data science.

Which Path Is Right for You?

Choosing between data science and AI comes down to the work you want to do every day. Use the questions below to identify your strongest fit.

Quick Self-Check

Do you prefer answering questions or building systems?

• Data science: Investigate patterns, test hypotheses, and explain what the data shows.
• AI: Design, train, and improve systems that learn and make decisions.

Do you prefer collaborating with stakeholders or spending more time building and refining code?

• Data science: Frequent collaboration with stakeholders, plus presenting findings and recommendations.
• AI: More engineering time, with collaboration focused on technical teams and delivery.

How do you handle ambiguity?

• Data science: Messy data and open-ended questions are common. You’ll make judgment calls and defend your approach.
• AI: Goals are often tied to performance targets, but you still iterate through experiments to hit them.

Background and Preparation

Your existing skills can signal which path will feel more intuitive at first.

• If you enjoy statistics, experimentation, and explaining results, data science often clicks faster.
• If you enjoy algorithms, building projects, and optimizing code, AI may feel more natural early on.

Either way, you can build toward the overlap. Many students start in one lane and add the other through projects, electives, and internships.

Market Outlook

Demand for both fields is strong. The U.S. Bureau of Labor Statistics projects 34 percent growth for data scientists over the next decade, compared with about three to four percent for occupations overall.

There is no standalone “AI engineer” category, but related roles such as computer and information research scientists are projected to grow by 20 percent, well above average.

Both paths show long-term momentum. Base your decision on the work you want to do, not on fear of limited demand.

How to Get Started

The best way to decide between data science and AI is to try the work yourself. Before committing to a degree, build a small project and see which process you enjoy more.

For Aspiring Data Scientists

Step 1. Start with a dataset that interests you. Pick a topic you care about and try to answer three questions using basic analysis. You’ll quickly learn whether you enjoy investigating patterns and interpreting results.

Step 2. Learn SQL and basic Python. These two tools power most entry-level analytics roles. With focused study, you can build functional proficiency in a matter of weeks.

Step 3. Build a portfolio project. Analyze restaurant inspection data in your city, performance trends for a sports team, or patterns related to a personal interest. What counts is your ability to extract insight and communicate it clearly.

Step 4. Pursue structured training. If you enjoy the work, a degree program can deepen your statistical foundation, expand your portfolio, and connect you to internships and mentors who accelerate your growth.

For Aspiring AI Engineers

Step 1. Complete a foundational machine learning course. Focus on neural networks, algorithms, and practical implementation.

Step 2. Implement algorithms from scratch. Build a simple neural network or decision tree without relying entirely on high-level libraries. Writing core logic strengthens your understanding of how models function.

Step 3. Contribute to an open-source AI project. Working in real codebases exposes you to software engineering standards, debugging practices, and collaborative development workflows.

Step 4. Build depth through formal study. Advanced AI roles require strong mathematical foundations and systems design skills. A structured program provides guided projects, research opportunities, and industry exposure that self-study alone rarely offers.

Get Career-Ready in New York

Data science and artificial intelligence begin from different angles, but long-term success comes from understanding both. The professionals who stand out can analyze data, build models, and translate results into systems that create impact.

Your degree determines where you start. Your adaptability determines how far you go.

Companies across finance, healthcare, media, and technology hire talent in both fields. At Pace University’s Seidenberg School of Computer Science and Information Systems, you build technical depth in a setting connected directly to that ecosystem.

Explore the program that aligns with your goals and take the first step toward a career in data science or AI.

You know you want to study computer science. You’ve looked at programs, compared campuses, and started imagining yourself writing code for a living. Then a question stops you: Should you pursue a bachelor of arts (BA) or a bachelor of science (BS)?

Either degree leads to a career in technology, and both cover core ground in programming, algorithms, and problem-solving. The difference comes down to how your coursework is structured, how much room you have to explore outside your major, and how deeply you specialize in technical subjects before graduation.

This guide breaks down the similarities, differences, and career implications of a BA and BS in computer science so you can choose the path that fits your goals.

Why Is There a Difference Between a BS and a BA?

The bachelor of arts is one of the oldest academic credentials, dating back to European universities in the 12th and 13th centuries. The bachelor of science came much later, emerging in the 1800s as scientific and technical disciplines earned their own recognition within higher education.

Whether a program awards a BA or a BS often depends on the college or school within a university that houses it. A college of engineering may grant a BS, while a college of arts and sciences may grant a BA. Some schools, such as Pace University’s Seidenberg School of Computer Science and Information Systems, offer both options under the same roof, enabling students to choose based on their academic and professional priorities.

Today, BS programs in computer science tend to emphasize deeper coursework in STEM fields. BA programs pair core computer science training with a broader range of courses in the humanities, social sciences, and other disciplines.

What Do Computer Science BS and BA Degrees Have in Common?

Before examining how these BS and BA degrees differ, it helps to understand how much they share. At Pace, both programs are housed within Seidenberg, both are STEM-designated, and both require 120 credits over a standard four-year timeline.

Students in either program take foundational courses that cover the same ground:

• CS 121 Introduction to Computer Science
• CS 113 Mathematical Structures for Computer Science
• CS 122 Object-Oriented Programming
• CS 232 Computer Organization
• CS 241 Data Structures and Algorithms
• CS 242 Algorithms and Computing Theory
• CS 491 Software Engineering

Calculus I and a probability/statistics course are also part of each curriculum. Graduates from either track qualify for entry-level roles in software engineering, web development, cybersecurity, data analysis, and IT.

The workload is comparable. Whichever track you choose, expect to write code, work through complex problems, and build real projects.

What Are the Differences Between a BS and BA Degree?

The differences center on how much technical coursework you take, how much room you have to study outside your major, and how each program structures its requirements. At Pace, the contrast is clear in the credit breakdown.

BS in Computer Science

The Computer Science BS requires a heavier load of computer science and math courses. Students complete courses in programming languages, operating systems, internet and distributed computing, and research methods as part of the required curriculum, along with Calculus II (or Mathematical Foundations of Machine Learning) and a lab science course. The program is accredited by ABET’s Computing Accreditation Commission, which can matter for certain engineering-adjacent roles and graduate programs.

If you already know you want to go deep on technical subjects, the BS gives you the structure to do that. Pace’s BS program offers specialized tracks in AI, cybersecurity, mobile app development, and game programming.

BA in Computer Science

The BA degree requires fewer CS credits and does not mandate the same depth of math and science. Instead, it requires a minor, which gives students the flexibility to build a second area of expertise alongside computer science. Popular minor choices at Pace include Digital Design, Economics, Marketing, and General Business.

Seidenberg also offers minors in Cybersecurity and Game Development, which students in either program can pursue. The cybersecurity minor covers network defense, threat analysis, and security management, while the game development minor focuses on design, interactive media, and game programming. BA students can use either to fulfill their required minor, and BS students can add one to complement their technical coursework.

If you see yourself working where technology meets business strategy, media, design, or communications, the BA gives you room to develop both skill sets during your undergraduate years.

Careers in Computer Science

The job market for computer science graduates is strong. The U.S. Bureau of Labor Statistics projects that employment in computer and information technology occupations will grow much faster than the average for all occupations by 2034, with approximately 317,700 openings each year. The median annual wage for this group was $105,990 in May 2024.

BA and BS graduates qualify for many of the same roles. The difference tends to show up in where you work, not what you do. A BS graduate might join a dedicated engineering team at a tech firm, while a BA graduate might bring CS skills to a marketing technology company, a media organization, or a consulting firm. Earning potential is strong on either track, especially in the New York metro area.

Career Paths for BS and BA Computer Science graduates

Here are common roles for computer science graduates, with salary data specific to the New York City area.

*Salary data sourced from Glassdoor, February 2026. Actual compensation varies based on employer, experience, and role scope.

Some specializations do lean toward one track. Roles in AI research, systems engineering, or cybersecurity architecture tend to favor candidates with the heavier technical preparation that a BS provides. Roles that blend technology with strategy, communication, or creative work (think UX research, product management, or tech consulting) tend to reward the cross-disciplinary background that a BA supports.

That said, employers also care about what you can do. Internships, personal projects, and applied coursework carry significant weight in hiring decisions no matter which letters appear on your diploma.

Which Degree Is Right for You?

There is no wrong answer here. The right degree depends on your goals. These questions can help you sort it out.

• How certain are you about a career in a technical CS specialty? If you already know you want to focus on AI, systems architecture, or cybersecurity, the BS provides the technical depth that supports those paths. If you’re drawn to CS but also interested in business, design, or another discipline, the BA lets you specialize in both simultaneously.
• How do you feel about advanced math? The BS requires Calculus II and a science lab course. The BA does not. Students who enjoy math and science will find the BS structure a natural fit. Students who prefer to balance technical coursework with other subjects may prefer the BA.
• Do you want to pursue a minor or double major? The BA requires a minor, and its lighter CS load makes it easier to add coursework in another area. The BS fills more of your schedule with CS and math courses, leaving less room for outside study.
• Are you considering graduate school? Both degrees can lead to a master’s program or a PhD in computer science. The BS may provide a slight head start on prerequisites for research-intensive programs. The BA can set you up for interdisciplinary graduate work in areas such as technology management or digital media.

Majoring in Computer Science at Pace University

Pace’s Seidenberg School of Computer Science and Information Systems offers both a BS and BA in Computer Science, along with related programs in cybersecurity, game development, information systems, and information technology. Both computer science programs are available at Pace’s New York City and Westchester campuses.

Seidenberg is ranked among the top computer science programs nationally by U.S. News and World Report, and Pace is ranked in the top six percent of universities for return on investment by the Georgetown University Center on Education and the Workforce.

No matter which degree you choose, you get the same Seidenberg resources and support. That includes access to programs and facilities that strengthen your education and career preparation:

• The Augmented Intelligence Lab, which combines research, education, and partnerships focused on how people interact with and are influenced by AI technologies
• The Cyber Range, a training environment for real-world security scenarios
• Faculty conducting active research in AI, machine learning, medical image analysis, robotics, and human-centered computing
• Internship pipelines to companies such as Google, IBM, Amazon, JPMorgan Chase, Microsoft, and Capital One
• Career services and programs like INSPIRE and ASPIRE that support career readiness from your first year through graduation
• Student organizations, hackathons, and international programs, including the New York City Design Factory

Pace’s location in New York City also provides direct access to one of the largest tech job markets in the country, with internship and networking opportunities across finance, healthcare, media, and technology.

Students who want to continue their education can also explore Seidenberg’s graduate programs, including the MS in Computer Science, the PhD in Computer Science, and the BS in Professional Computer Studies for working adults.

FAQs

Which pays more, a BS or a BA in computer science?

A BS in computer science does not automatically pay more than a BA in computer science. Salary depends far more on your role, employer, industry, and experience level than on which degree type you hold. Both degrees qualify graduates for well-paying positions in software engineering, data analysis, cybersecurity, and IT. In the New York metro area, entry-level computer science roles regularly start above $80,000. Over time, specialization and career growth matter more than the letters on your diploma.

Is a BA or BS in computer science harder?

A BS in computer science is not necessarily harder than a BA, but it does require more technical coursework. The core CS classes overlap significantly, and each program demands strong analytical and programming skills. The BS adds courses in areas like operating systems and programming languages, plus more advanced math (including Calculus II at Pace), which some students find more challenging. The BA balances computer science with a required minor and broader elective options, which creates a different kind of academic demand. Neither program is easy, and both require sustained effort.

Can I work in IT with a BA in computer science?

Yes, you can work in IT with a BA in computer science. A BA provides the programming, analytical, and problem-solving skills that IT employers look for. Many IT roles, including IT specialist, systems analyst, and web developer, are open to graduates with either a BA or a BS. Practical experience through internships and projects often carries as much weight as the specific degree type.

Can I go to graduate school with a BA in computer science?

Yes, you can attend graduate school with a BA in computer science. Both a BA and a BS can serve as the foundation for a master’s degree or PhD in computer science. Some research-intensive graduate programs may require additional math or science preparation, which BS graduates may already have completed. BA graduates can typically fill any gaps through prerequisite courses or bridge programs.