predictive models jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, predictive models appears in 190 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning predictive models, with demand share down 9.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want predictive models?
Upload your resume and Skillenai will show which roles your predictive models experience fits, which skills you already cover, and what is missing.
Frequently asked questions about predictive models
+Is predictive models in demand in 2026?
Yes. predictive models appears in 190 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Data Scientist accounts for the most postings mentioning predictive models (20.5% of all postings mentioning predictive models).
+What jobs require predictive models?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning predictive models are AI Ops Engineer (7.7% of that role’s postings mention predictive models), ML Scientist (5.4% of that role’s postings mention predictive models), Quantitative Research Intern (4.5% of that role’s postings mention predictive models).
+What skills are commonly paired with predictive models?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), predictive models most often appears alongside Python, machine learning, SQL, R, data visualization.
+Where is predictive models most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring predictive models are New York City, Chicago, San Francisco, London, Alexandria, according to the Skillenai jobs index.
+How can I keep up with new predictive models content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning predictive models alongside the jobs index. You can subscribe to a daily email digest of new predictive models content from your Skillenai account.
+Which skills come before and after predictive models?
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 predictive models — last 90 days
Career paths around predictive models
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before predictive models
Skills after predictive models
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 | 46 |
| Before: sql | 39 |
| Before: Power BI | 25 |
| Before: Tableau | 23 |
| Before: Excel | 14 |
| Before: dashboards | 9 |
| Before: R | 9 |
| Before: numpy | 8 |
| After: sql | 20 |
| After: Power BI | 18 |
| After: python | 16 |
| After: Tableau | 14 |
| After: AWS | 10 |
| After: Excel | 9 |
| After: machine learning | 9 |
| After: ETL pipelines | 8 |
Roles most likely to require predictive models
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| AI Ops Engineer | 2 | 7.7% |
| ML Scientist | 2 | 5.4% |
| Quantitative Research Intern | 1 | 4.5% |
| AI & Automation Engineer | 1 | 4.2% |
| People Analytics Manager | 1 | 3.8% |
| Data Science Director | 2 | 3.2% |
| AI Transformation Manager | 1 | 3.1% |
| Applied AI Scientist | 1 | 3.0% |
| Marketing Analytics Manager | 2 | 3.0% |
| ML Ops Engineer | 1 | 2.8% |
Roles with the most predictive models postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 39 | 20.5% |
| Data Analyst | 10 | 5.3% |
| Product Manager | 6 | 3.2% |
| Software Engineer | 5 | 2.6% |
| AI Engineer | 3 | 1.6% |
| Business Intelligence Analyst | 3 | 1.6% |
| Quant Research Intern | 3 | 1.6% |
| Solution Engineer | 3 | 1.6% |
| AI Ops Engineer | 2 | 1.1% |
| Analytics Director | 2 | 1.1% |
Top companies posting jobs requiring predictive models
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Bah | 4 |
| Cisco | 4 |
| ProSidian Consulting | 3 |
| RelationalAI | 3 |
| Sixt | 3 |
| Vanguard | 3 |
| Anduril Industries | 3 |
| PubMatic | 3 |
| AbbVie | 3 |
| Eli Lilly and Company | 3 |
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 predictive models
| Name | Postings | Share |
|---|---|---|
| New York City | 9 | 4.7% |
| Chicago | 6 | 3.2% |
| San Francisco | 6 | 3.2% |
| London | 4 | 2.1% |
| Alexandria | 3 | 1.6% |
| Cambridge | 3 | 1.6% |
| Lakes by the Bay | 3 | 1.6% |
| San Jose | 3 | 1.6% |
| Toronto | 3 | 1.6% |
Skills commonly paired with predictive models
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 2026-10-29. 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 predictive models by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s predictive models postings by all predictive models 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: 0.1% to 0.1%. 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 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (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
- 8f8c637a5cec413c
- data_as_of
- 2026-10-29
- window_days
- 90