classification jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-05, classification appears in 878 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning classification, with demand share down 4.5% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-05
Which roles want classification?
Upload your resume and Skillenai will show which roles your classification experience fits, which skills you already cover, and what is missing.
Frequently asked questions about classification
+Is classification in demand in 2026?
Yes. classification appears in 878 job postings indexed by Skillenai over the 90 days ending 2026-11-05. Data Scientist accounts for the most postings mentioning classification (37.9% of all postings mentioning classification).
+What jobs require classification?
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 classification are Quantitative Engineer (18.2% of that role’s postings mention classification), Data Science Lead (17.4% of that role’s postings mention classification), Product Data Scientist (16.2% of that role’s postings mention classification).
+What skills are commonly paired with classification?
Across job postings indexed by Skillenai (90 days ending 2026-11-05), classification most often appears alongside Python, machine learning, SQL, regression, clustering.
+Where is classification most in demand?
As of 2026-11-05, the metro areas posting the most jobs requiring classification are San Francisco, London, Bengaluru, New York City, Hyderabad, according to the Skillenai jobs index.
+How can I keep up with new classification content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning classification alongside the jobs index. You can subscribe to a daily email digest of new classification content from your Skillenai account.
+Which skills come before and after classification?
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 classification — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around classification
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before classification
Skills after classification
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: python | 18 |
| Before: Tableau | 14 |
| Before: sql | 13 |
| Before: scikit-learn | 7 |
| Before: machine learning | 7 |
| Before: R | 7 |
| Before: docker | 6 |
| Before: ci/cd | 5 |
| After: python | 12 |
| After: Power BI | 7 |
| After: databricks | 6 |
| After: feature engineering | 5 |
| After: tensorflow | 5 |
| After: PySpark | 5 |
| After: sql | 5 |
| After: xgboost | 5 |
Roles most likely to require classification
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Quantitative Engineer | 4 | 18.2% |
| Data Science Lead | 4 | 17.4% |
| Product Data Scientist | 6 | 16.2% |
| Principal Data Scientist | 5 | 13.2% |
| Prompt Engineer | 5 | 8.9% |
| Data Science Analyst | 2 | 8.3% |
| Tech Lead Manager | 2 | 8.0% |
| Data & AI Engineer | 2 | 7.7% |
| Perception Engineer | 2 | 7.1% |
| Data Science Director | 4 | 6.8% |
Roles with the most classification postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 333 | 37.9% |
| Machine Learning Engineer | 89 | 10.1% |
| Software Engineer | 43 | 4.9% |
| ML Engineer | 36 | 4.1% |
| AI Engineer | 24 | 2.7% |
| Data Analyst | 24 | 2.7% |
| Product Manager | 13 | 1.5% |
| Data Science Manager | 12 | 1.4% |
| Technical Program Manager | 12 | 1.4% |
| Research Engineer | 8 | 0.9% |
Top companies posting jobs requiring classification
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Capital One | 81 |
| Anduril Industries | 24 |
| Tripadvisor | 13 |
| Bjakcareer | 13 |
| Bosch | 12 |
| CLERA | 11 |
| Elsevier | 9 |
| Stripe | 7 |
| NielsenIQ | 7 |
| Xometry | 7 |
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 classification
| Name | Postings | Share |
|---|---|---|
| San Francisco | 28 | 3.2% |
| London | 23 | 2.6% |
| Bengaluru | 20 | 2.3% |
| New York City | 20 | 2.3% |
| Hyderabad | 15 | 1.7% |
| Toronto | 13 | 1.5% |
| McLean | 12 | 1.4% |
| Boston | 9 | 1.0% |
| Amsterdam | 8 | 0.9% |
Skills commonly paired with classification
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 classification by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s classification postings by all classification 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). Adjusted posting share: 0.4% to 0.4%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- 5bd6e254d57c18ea
- data_as_of
- 2026-11-05
- window_days
- 90