probability jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-05, probability appears in 418 job postings indexed by Skillenai over the past 90 days — Software Engineer has the most postings mentioning probability.
Last updated · 90d ending 2026-11-05
Which roles want probability?
Upload your resume and Skillenai will show which roles your probability experience fits, which skills you already cover, and what is missing.
Frequently asked questions about probability
+Is probability in demand in 2026?
Yes. probability appears in 418 job postings indexed by Skillenai over the 90 days ending 2026-11-05. Software Engineer accounts for the most postings mentioning probability (18.4% of all postings mentioning probability).
+What jobs require probability?
According to the Skillenai jobs index over the 90 days ending 2026-11-05, among roles with at least 20 postings, the highest shares mentioning probability are Quantitative Research Intern (17.4% of that role’s postings mention probability), Algorithm Engineer (16.7% of that role’s postings mention probability), Deep Learning Engineer (11.1% of that role’s postings mention probability).
+What skills are commonly paired with probability?
Across job postings indexed by Skillenai (90 days ending 2026-11-05), probability most often appears alongside Python, statistics, linear algebra, Machine Learning, C++.
+Where is probability most in demand?
As of 2026-11-05, the metro areas posting the most jobs requiring probability are San Francisco, New York City, London, Waltham, Toronto, according to the Skillenai jobs index.
+How can I keep up with new probability content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning probability alongside the jobs index. You can subscribe to a daily email digest of new probability content from your Skillenai account.
+Which skills come before and after probability?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring probability — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around probability
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before probability
Skills after probability
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: C++ | 2 |
| Before: visualizations | 1 |
| Before: predictive analytics | 1 |
| Before: Convolutional neural network (CNN) | 1 |
| Before: predictive metadata | 1 |
| Before: images | 1 |
| Before: data virtualization | 1 |
| Before: emigration | 1 |
| After: sql | 2 |
| After: python | 2 |
| After: HTML | 2 |
| After: Redux | 1 |
| After: income prediction modeling | 1 |
| After: data science and analytics | 1 |
| After: Data Science | 1 |
| After: Theoretical Physics | 1 |
Roles most likely to require probability
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Quantitative Research Intern | 4 | 17.4% |
| Algorithm Engineer | 7 | 16.7% |
| Deep Learning Engineer | 4 | 11.1% |
| Quant Developer | 2 | 9.5% |
| Quantitative Engineer | 2 | 9.1% |
| Quantitative Trader | 4 | 8.5% |
| Quantitative Researcher | 12 | 8.2% |
| Senior Data Scientist | 2 | 7.4% |
| Quantitative Strategist | 2 | 6.9% |
| Machine Learning Researcher | 2 | 6.2% |
Roles with the most probability postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Software Engineer | 77 | 18.4% |
| Data Scientist | 59 | 14.1% |
| Machine Learning Engineer | 26 | 6.2% |
| Research Scientist | 19 | 4.5% |
| ML Engineer | 13 | 3.1% |
| Quantitative Researcher | 12 | 2.9% |
| Applied Scientist | 10 | 2.4% |
| Systems Engineer | 8 | 1.9% |
| Algorithm Developer | 7 | 1.7% |
| Algorithm Engineer | 7 | 1.7% |
Top companies posting jobs requiring probability
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Anduril Industries | 93 |
| Thinking Machines | 16 |
| Waabi | 8 |
| Northrop Grumman | 8 |
| General Motors | 7 |
| Ataraxis AI | 6 |
| Faculty | 5 |
| True Anomaly | 5 |
| Drweng | 4 |
| Gravitonresearchcapital | 4 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for probability
| Name | Postings | Share |
|---|---|---|
| San Francisco | 26 | 6.2% |
| New York City | 23 | 5.5% |
| London | 17 | 4.1% |
| Waltham | 10 | 2.4% |
| Toronto | 9 | 2.2% |
| Broomfield | 7 | 1.7% |
| San Jose | 7 | 1.7% |
| Washington | 7 | 1.7% |
| Fort Collins | 6 | 1.4% |
Skills commonly paired with probability
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-11-05. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning probability by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s probability postings by all probability postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). An adjusted trend is not shown because comparable posting coverage is insufficient.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 7ccbd93edc9f5f78
- data_as_of
- 2026-11-05
- window_days
- 90