The most apparent reason to consider a PhD in mathematics is the salary. For private industry jobs, math PhDs earn high starting wages. In fact, salaries for math PhDs are among the highest for any advanced degree, especially when employed in technology.
According to PayScale.com, the average salary for math PhDs is $106,000.
One reason salaries are so high: a PhD in mathematics qualifies individuals for some of the best-paying jobs in data science and machine learning. For example, PhDs in math commonly land senior data scientist roles ($130,000 / year) and machine learning engineer jobs ($120,000).
Yet, beyond salary, you might be wondering: Is a PhD in mathematics worth it? Sure, the degree results in a salary bump, but the trade-offs include opportunity costs (these programs usually take 3-5 years to complete) and they do require you to learn skills on your own like analytics, SQL and Python.
Below we’ve taken a closer look at math PhD salaries. Read on for a look at salaries by job title, PhD in math salaries vs. other fields, and if a PhD in math is good for a data science career. Afterwards we explore the other considerations for your decision making.
See the most up-to-date Data Science Salary statistics.
Math PhDs that seek private industry jobs tend to fall into three areas: IT, finance, or business administration. In terms of IT careers, some of the best starting salaries for math PhDs are in data science, with many jobs starting at $120,000+.
Here is a look at some of the top private industry data science jobs for math PhDs, as well as their starting salaries in data science:
PhDs in math earn some of the highest wages of all STEM PhDs, behind just a small set of subjects including computer science and engineering. The industry you enter does have a significant effect on salary however.
Almost all PhDs who go into teaching and academia, for example, earn a similar median wage around $75,000. For private industry jobs, PhDs earn significantly more.
According to the 2019 Survey of Doctorate Recipients, math PhDs have the fourth-highest median salary once they are in the field of IT:
Source: National Center for Science and Engineering Statistics
According to the survey, math PhDs also command high median salaries in the fields of management, sales or administration ($164,000) and professional services ($142,000). Considering another PhD program? See our salary guides for PhD in economics and PhD in statistics.
Mathematics PhDs receive training and experience that is relevant to data science careers, and provide training to others on a number of subjects that data scientists use every day. For instance, a PhD program in math would provide training in:
In particular, many tech companies hire PhDs for research and development (R&D) roles. A PhD is often a required or preferred qualification, especially in the roles of Research Scientist and Machine Learning Scientists for example. In these positions the main responsibility of the scientist is to help advance machine learning or data science exploration for the company.
As such, PhDs in data science roles typically research and/or develop new algorithms or create machine learning libraries. They can also be asked to provide leadership on a niche specialization like machine learning, computer vision, or robotics.
With advanced degrees becoming increasingly common in data science – within the industry more than 80% have a master’s or PhD – a math PhD can help make you more competitive for a data science job. In fact, mathematics is the No. 2 subject amongst data scientists holding a PhD, according to the Burtch Works 2021 Salary survey:
Looking only at median salary, a math PhD is a valuable investment. But the PhD cannot be taken in a vacuum, and it is important to evaluate the other skills you may need to invest in for a private sector employer. Programming and SQL knowledge being high among them.
To help understand some of the other aspects of pursuing this advanced degree, we talked with a math PhD who now works in the data science field. They highlighted five areas to give thought to before committing to a program:
Consider a master’s first - An accelerated master’s program can help prepare you for a PhD, as well as help you better understand if you want to make the commitment. And you can complete a master’s program in as little as one year.
Do you like teaching? - Math PhDs typically are required to teach undergraduate students. If you are not interested in teaching, or you don’t like it, a PhD in math might not be right for you.
Why do you want to pursue a PhD? Answer this question. You should be able to articulate exactly why you want to make this commitment, and you should be able to explain it to someone outside of the field. If you understand your motivations, you will be more likely to succeed.
Consult with an advisor - Ask a potential advisor about the work they do. This will help you better understand the PhD research component. Hint: It should interest and excite you. If it sounds tedious, chances are, you likely won’t excel in a PhD program.
Know where you want to focus - If you are interested in a career in data science, you should focus your research in an area that is applicable to the career you want. Some research areas that are applicable to data science include: algorithms, machine learning, deep learning and artificial intelligence.
Beyond these questions, you also have to bear the opportunity costs. Completing a PhD requires a significant investment of time, typically five to six years, and can incur significant expenses, both in the direct (tuition) or indirect (delayed earnings).
Let’s briefly expand on those costs. PhD candidates usually earn a stipend of about $35,000 / year, and depending on the program, may have to pay tuition. If you stack that up against the earnings you are delaying – starting salaries for master’s in data science programs can start at $126,000 / year – you can calculate as your opportunity cost roughly $90,000 / year, starting your second year (the first year would be required for an accelerated master’s regardless).
Weigh all of the factors we’ve discussed here in your decision: starting and median salary, on the job responsibilities and opportunities, alternative pathways to your goals, and associated costs. With all of this aggregated knowledge and the linked resources we’ve sprinkled throughout, you are well on your way to making the best choice for your career!