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Job outlook for mathematical engineers in the United States
Are mathematical engineer jobs in demand?
Mathematical engineer job and salary trends over time
Mathematical engineer jobs over time
Mathematical engineer job growth rate over time
| Year | # of jobs | % of population |
|---|---|---|
| 2021 | 45 | 0.00% |
| 2020 | 45 | 0.00% |
| 2019 | 45 | 0.00% |
| 2018 | 42 | 0.00% |
| 2017 | 39 | 0.00% |
Average mathematical engineer salary over time
Mathematical engineer salary by year
| Year | Avg. salary | Hourly rate | % Change |
|---|---|---|---|
| 2026 | $90,641 | $43.58 | +4.4% |
| 2025 | $86,836 | $41.75 | +2.1% |
| 2024 | $85,030 | $40.88 | --0.3% |
| 2023 | $85,266 | $40.99 | --0.1% |
| 2022 | $85,310 | $41.01 | +1.5% |
Mathematical engineer jobs by state
Most common states for mathematical engineers
| Rank | State | Population | # of jobs | Employment/ 1000ppl |
|---|---|---|---|---|
| 1 | District of Columbia | 693,972 | 190 | 27% |
| 2 | Delaware | 961,939 | 74 | 8% |
| 3 | Virginia | 8,470,020 | 618 | 7% |
| 4 | Washington | 7,405,743 | 460 | 6% |
| 5 | Massachusetts | 6,859,819 | 400 | 6% |
| 6 | Colorado | 5,607,154 | 350 | 6% |
| 7 | Oregon | 4,142,776 | 244 | 6% |
| 8 | California | 39,536,653 | 1,951 | 5% |
| 9 | Utah | 3,101,833 | 140 | 5% |
| 10 | New Hampshire | 1,342,795 | 49 | 4% |
| 11 | Vermont | 623,657 | 22 | 4% |
| 12 | New York | 19,849,399 | 506 | 3% |
| 13 | Illinois | 12,802,023 | 372 | 3% |
| 14 | North Carolina | 10,273,419 | 285 | 3% |
| 15 | New Jersey | 9,005,644 | 229 | 3% |
| 16 | Arizona | 7,016,270 | 226 | 3% |
| 17 | Idaho | 1,716,943 | 49 | 3% |
| 18 | Rhode Island | 1,059,639 | 36 | 3% |
| 19 | Alaska | 739,795 | 22 | 3% |
| 20 | Wyoming | 579,315 | 19 | 3% |
Most common cities for mathematical engineers
| Rank | City | # of jobs | Employment/ 1000ppl | Avg. salary |
|---|---|---|---|---|
| 1 | Santa Clara | 1 | 1% | $108,333 |
Mathematical Engineer job outlook: Expert opinions
Our panel of mathematical engineer experts
University of Texas of the Permian Basin
Paul Feit
Professor and ChairUniversity of Texas of the Permian Basin
Department of Math and Physics
Will there be an enduring impact of the coronavirus pandemic on Mathematical Engineers?
Paul Feit: The risk of long term disruption is financial. If business sectors collapse, as in 2008, it may take five years to recover. But if the majority of companies merely take a financial hit (instead of bankruptcy), life will return to normal very quickly.
The fields related to disease control will probably undergo a hiring boost. This covers both medical science but also statistical analysis. People will debate for years about the significance of different social strategies (like masking).
Are there any particularly good places in the United States for Mathematical Engineers to find work opportunities?
Paul Feit: A bachelor's or minor in Mathematics is sort-of a ‘vanilla’ degree. It is a boost in many careers; it tells potential employers that the graduate is ready for the quantitative needs in any position. Just about every position has some quantitative need. A minor in Mathematics alerts employers that the candidate brings computational finesse to whatever his/her major.
Jobs for higher degrees are in academic institutions and companies big enough to need analysis of their proprietary data. Computer companies appreciate mathematical credentials, but may expect these to be in addition to a computer science background.
How do you envision technology impacting Mathematical Engineers in the next 5 years?
Paul Feit: Ironically, technology tends to conceal mathematics. Once the solution to a problem becomes an algorithm, it is reduced to a button click.
The impact on the field depends on the application.
- Research. Computing power has enabled mathematicians to look at more examples of a problem than ever before. This change actually began in the 1990s. The goal of theoretical mathematics is to establish firm theorems, but new results often come by inspecting examples for a trend.
- Business. ‘Big data’ is the buzzword for the exciting opportunity to produce new strategies by analyzing more data than was possible in the past. There is a question mark over this area. Converting the idea into practice has been harder than one might hope. (A historical precedent is Artificial Intelligence. That idea seemed within grasp in the 1950s, but it has defied repeated attacks.)
- Warning: The following may sound snide. It is not meant to be. Many companies still rely on old software for certain computations. Much of the technical software was programmed before computing power made it truly useable (that is, it could not actually crack the relevant problem in real-time). Some of these drafts are rife with mistakes. Updating is a double challenge: you need a good programmer AND someone with sufficient understanding of theory to spot and correct mistakes.