Is math essential to working in AI?

Mathematical reasoning is crucial for many AI fields , especially data science, machine learning, and deep learning. Key math courses include linear algebra, probability and statistics, and calculus. While some AI roles, like AI product management, may require less math, technical positions rely heavily on these concepts.

Do you need a master’s or PhD to work in data science?

While a master's degree or PhD may enhance your prospects, these credentials are not required to work in data science . However, many companies hiring mid-to-senior roles may prefer candidates to have at least a master's degree, and research-based roles or academia often require a PhD. Those without a graduate degree may qualify for data science careers via an alternative pathway, like a MicroMasters program.

Can I work in AI without a degree?

You can pursue a career in AI without a formal degree, although employers may prefer candidates with at least a bachelor's degree in computer science, engineering, or a related field for specific roles. To enter the field of AI, focus on self-learning through online education and work on hands-on projects to showcase your skills with a portfolio.

Do you need a master’s or PhD for AI careers?

By: Janice Mejías Avilés, Edited by: Marie Custodio Collazo

Published: March 12, 2025

Careers in artificial intelligence (AI) and machine learning (ML) are growing rapidly as industries seek to adopt AI solutions. The World Economic Forum's 2025 Future of Jobs Report projects that by 2030, big data, AI, and machine learning will drive the fastest-growing jobs.

As professionals rush to upskill or reskill to meet this demand, a common question arises: What degree do you need for a career in AI?

What do you need to work in AI?

Artificial intelligence is a still-evolving field, and the educational requirements for AI careers vary widely depending on the role and industry.

To understand current hiring trends, let's look at data from Lightcast, a labor market analytics firm. According to data from February 2024 to February 2025, employers hiring software engineering roles in the United States typically listed the following education level requirements in job postings:

Bachelor's degree: 67%
Master's degree: 22%
Doctor of philosophy (PhD)/professional degree: 5%

By comparison, AI engineering roles during the same period showed different degree requirements in its job postings:

Bachelor's degree: 54%
Master's degree: 43%
PhD/professional degree: 23%

Note: According to Lightcast's job posting analytics methodology, the total percentage may exceed 100% as some job postings may list multiple educational requirements, allowing candidates with any listed degrees to be considered eligible.

A bachelor's degree can serve as an entry point into the tech industry. However, job market data suggests that holding a master's or doctoral degree may enhance competitiveness for AI engineer roles, as employers may prioritize candidates with advanced degrees in these specialized fields.

Despite these trends, many AI roles may prioritize hands-on experience with AI tools and value alternative credentials.

For professionals with a bachelor's degree in computer science, engineering, or related fields, upskilling through specialized AI training — such as courses and certificate programs — may be the most efficient path, as they already have a strong technical foundation.

For career changers from a non-technical background, reskilling through structured programs — often while working full-time — may be necessary to bridge the AI skills gap.

Ultimately, the best learning pathway depends on your career goals within the AI field and your prior experience.

Balancing self-study and formal education

Not everyone has the time or financial resources to pursue a master's degree or a PhD. Likewise, self-study alone may not provide the structure or credentials needed to break into AI.

A middle-ground approach that combines formal education with flexibility is a micro-credential, such as a MicroMasters® program.

For those seeking a structured yet flexible AI learning experience, the MITx MicroMasters Program in Statistics and Data Science offers a rigorous foundation in statistics, machine learning, and data analysis — all critical competencies for AI roles.

This five-course graduate-level program from MIT is open to learners worldwide, requires no application, and is entirely online with no set class times, allowing professionals to study while working. Completing and passing the program also provides a pathway to earn college credits toward more than 50 graduate degrees worldwide, including a PhD from MIT.

Through hands-on experience with real-world datasets, MITx MicroMasters Program in Statistics and Data Science prepares learners to gain practical AI skills needed for roles such as:

AI/ML engineer
Business intelligence analyst
Data analyst
Data engineer

Do you need an advanced degree to get a job in AI?

Your path will vary based on which AI specialization you pursue, the industry, your academic background, and your prior experiences with tech tools.

While some positions, such as AI research scientists, often require a master's or PhD, many AI professionals enter the field through a mix of undergraduate education, alternative credentials, and hands-on experience.

An advanced degree can boost competitiveness, but it's not the only way forward. Whether through structured programs like the MITx MicroMasters, online courses, or self-directed projects, professionals can develop AI expertise in a way that fits their needs.

The next MITx MicroMasters course, Probability, starts on May 14 — a great opportunity to enroll and begin your AI learning journey.

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