Majoring in computer science used to be considered a safe career bet, ensuring stable pay and work. After the 2008 recession, many students turned to the field’s promising outlook: Between 2008 and 2024, the number of computer science degrees grew by almost 500%, according to The Washington Post.
During this period, computer science was the third-most-popular major behind nursing and psychology, making up almost 5% of early-career college graduates.
But the underemployment rate for CS graduates is 19%, according to 2024 Federal Reserve Bank of New York data. Recent tech layoffs across companies like Amazon, Meta, and Microsoft make the field look even less appealing. College students are taking note: A National Student Clearinghouse Research Center survey shows that undergraduate students enrolling in CS programs has dipped by 8.4%.
In a viral February X post, tech entrepreneur and engineer Matt Shumer told readers that he is “no longer needed for the actual technical work of [his] job.” He said some of the industry’s best engineers are already handing their coding work to artificial intelligence.
In an attempt to pivot, universities like Carnegie Mellon, MIT, and Northwestern have implemented AI majors. The Artificial Intelligence and Decision-Making major at MIT is now the second-largest one at the school.
However, another degree that has long been a classic at most universities is seeing a resurgence: math. Mathematics degree enrollment dipped after 2018 and bottomed out in 2022. But since then, its popularity has slowly risen, growing almost 2% by 2024, according to data from the Integrated Postsecondary Education Data System. At the same time, mathematics occupations are projected to grow 28.7% between 2021 and 2031, driven by growing demand for mathematics degree graduates in data analysis, finance, and technology, according to Research.com.
The mathematical advantage
High-paying roles for mathematicians are popping up across the tech and financial sectors. The rise of AI has put a premium on skills like stochastic calculus, linear algebra, and numerical methods.
Advanced mathematicians understand the foundational logic behind AI and can spot errors and biases. They can also use mathematical reasoning to write new financial algorithms—making them crucial to the leading quantitative firms, tech giants, and hedge funds.
The price tag on talent is high: AI giants like OpenAI and Anthropic now offer salaries that can range from $180,000 to $2 million, according to Forbes.
In turn, quant firms—which use advanced mathematical modeling to analyze financial data, optimize investment portfolios, and profit from market inefficiencies—have bumped up the pay to compete for the same mathematicians. Between 2024 and 2025, quant associate salaries grew 13%, according to eFinancialCareers. Associates aren’t alone: Quant VPs and directors saw salary growth of 10% and 18%, respectively. (By comparison, tech analyst jobs saw salaries drop by 36.3%, and associate comp dropped by 13.8%.)
Today, quant firms are offering new math grads starting salaries of $350,000 to $500,000. Even summer interns at quant firms are receiving massive starting packages, according to The Wall Street Journal.
“I think the biggest edge someone has is to be a mathematician that can code,” says Daniel Hughes, the director of quant research and trading at the recruitment firm Selby Jennings. “That is what I really like to work with. They’ve proven themselves to be able to sit with a problem for a very long time, typically five years for their PhD, and once they are working in Python . . . that’s when things get interesting.”
Don’t lose all hope
However, if your passion is in computer science rather than math, not all hope is lost. The secret might also be taking CS and coding skills to quant firms.
One quant recruiter, who asked to remain anonymous for fear of losing clients, said that the AI models at quant firms like Jane Street are only operating at the same level as the median intern.
The AI can’t quite run financial analysis the same way a seasoned mathematician might. While math geniuses refine the calculations, computer scientists might be needed to train the intelligence models.
Meanwhile, many financial funds have coding projects that still need a human in the loop. Instead of coding all day long, computer scientists at these institutions might train financial models or code AI subagents to complete more granular tasks.
If you are able to leverage new AI systems, computer scientists can make themselves pretty valuable to a quant team, according to the recruiter.
