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Finance

Including 455,670 postings from Fortune 500 career sites and 89,801 postings from small-employer job boards and 32,618 postings from US federal jobs, collected since 27 September 2026. Figures on this page are larger than the published ones and are not comparable with them.

How Finance relates to the other programs

16,180 of the 794,739 postings in every geography in the last 90 days count toward Finance (2.0%), the 7th largest of the 11 programs. 12,866 count toward it alone; 3,314 also count toward another program. 23.8% are AI-related, against 25.8% across all postings: the 9th highest of the 11 programs with enough postings to say.

Business Management: 87% in commonHuman Resource Management: 86% in commonAccounting: 79% in commonGlobal Supply Chain Management: 65% in commonEntrepreneurial Management: 50% in commonInformation Systems: 77% in commonExperience Design and Management: 67% in commonMarketing: 77% in commonStrategic Management: 78% in commonSales: 83% in commonFIN in the middle; the other programs overlap it in proportion to what they share.

Finance is the large circle. Each small circle overlaps it in proportion to how much of its work styles it shares with Finance: the most alike program has 80% of its area inside, a program with half that similarity half as much, and one that shares nothing just touches. Around the ring, programs sit next to the ones they share most with. The number in each circle is the exact figure; every circle opens that program.

Most alike on work styles

  1. Business Management87% in common

    Cooperation · Initiative · Analytical Thinking · Dependability · Stress Tolerance

  2. Human Resource Management86% in common

    Cooperation · Initiative · Dependability · Analytical Thinking · Stress Tolerance

  3. Sales83% in common

    Cooperation · Initiative · Dependability · Analytical Thinking · Independence

  4. Accounting79% in common

    Cooperation · Initiative · Analytical Thinking · Dependability · Stress Tolerance

  5. Strategic Management78% in common

    Cooperation · Initiative · Analytical Thinking · Dependability · Leadership

  6. Marketing77% in common

    Cooperation · Initiative · Analytical Thinking · Dependability · Stress Tolerance

  7. Information Systems77% in common

    Cooperation · Initiative · Analytical Thinking · Leadership · Independence

  8. Experience Design and Management67% in common

    Cooperation · Initiative · Analytical Thinking · Independence · Attention to Detail

  9. Global Supply Chain Management65% in common

    Cooperation · Analytical Thinking · Stress Tolerance · Dependability · Attention to Detail

  10. Entrepreneurial Management50% in common

    Initiative · Cooperation · Innovation · Stress Tolerance · Analytical Thinking

“In common” is the share of the two programs’ work styles mixes that they hold together: for each item, the smaller of its two shares, summed, over the larger, summed. Items shown are the ones both programs ask for most, each held by at least 20 postings in both. Last 90 days, every geography. A posting counts toward a program when the discipline classifier assigns it there (how postings are classified).

Where Finance postings fall across the four quadrants

Each bar counts Finance postings in one quadrant, not the whole quadrant. The filled part is how many of those are AI-related. The last figure on each row turns it round: how much of that whole quadrant of the labour market Finance postings make up. Last 90 days, by real posting date. Each quadrant’s name opens it on the overview, across every program.

What the four quadrants are cut on. The capabilities a posting asks for, read from its description. A role naming three or more distinct engineering capabilities has builder depth; three or more business capabilities gives business depth. Both is Hybrid, neither is AI-thin. There are four because two yes-or-no tests cross, which is what makes them quadrants rather than a list. The threshold of three is the one the source analysis used, adopted rather than reinvented so the two stay comparable.

The 16,180 postings drawn here are this program’s share of the panel: postings carrying a real posting date from the last 90 days in every geography that the discipline classifier assigns to it. A posting can count toward more than one program. Quadrants with fewer than 20 postings are left out, and AI shares are withheld below 200.

What each quadrant asks for

Ranked by how far Business-deep AI sits from the other quadrants, not by how often the item appears. The bar is this region; the ticks are the others. An item everybody asks for tells you nothing about where you are.

Tools

Named software, languages and platforms. The fastest thing on this page to learn.

Partly definitional. Quadrants are cut on how many of these terms a posting names, so a quadrant leads on the terms that put it there. The ordering is still informative; the level is not a discovery.

  • Pythonabout 4 in 100 postings ask for this
    4.0%−29.4 points vs the other two
  • Structured Query Language (SQL)about 10 in 100 postings ask for this
    10%−21.4 points vs the other two
  • Tableauabout 7 in 100 postings ask for this
    7.0%−15.4 points vs the other quadrant
  • Amazon Web Services (AWS)fewer than 1 in 100 postings ask for this
    0.4%−14.3 points vs the other quadrant
  • Azurefewer than 1 in 100 postings ask for this
    0.4%−13.9 points vs the other quadrant
  • Business intelligenceabout 4 in 100 postings ask for this
    4.1%−10.2 points vs the other quadrant
  • Data warehousingabout 1 in 100 postings ask for this
    0.9%−8.0 points vs the other quadrant
  • Databasesabout 5 in 100 postings ask for this
    4.9%−7.2 points vs the other two

Shares are of postings in this region over the last 90 days, by real posting date, in the geography chosen above. These figures were fixed to the United States until 22 September 2026 and could not follow that choice; they can now.

Where these numbers come from

19,016 Finance postings have been collected in all. The figures above use 16,180 of them: the last 90 days (16,164), and of those, the ones anywhere. Each bar is one source; 16 come from sources too small to draw.

The sources ask for AI at very different rates because they were collected for different reasons, so the 23.8% AI share at the top of the page is a mix of them and moves with that mix. Compare sources side by side rather than reading the pooled figure as the market’s. How each source is collected, and why they are never pooled for the market figures, is under the four sub-corpora on the research page.

What this page cannot tell you yet

No job titles from this program

The rules matching postings to this discipline were written without input from the people who know where these graduates go. Titles you supply become candidate rules, measured against the corpus before anything is published.

No employers from this program

None of the employers who actually hire your graduates have been named, so the panel can only describe firms that happen to post on the boards already collected. Named employers are resolved to their public job feed and collected directly.

Two things from you change all of the above. The job titles your graduates commonly take, and the employers who commonly hire them. Titles become candidate classifier rules; employers become collection targets, deduplicated across every program so the ones several of you name independently get resolved first.

Sign in to add them. Everything you enter is stored as a claim from your program, never merged into the posting evidence, and shown with your program named beside it.

How the panel is built · Methodology