predictive modeling jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, predictive modeling appears in 1,386 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning predictive modeling, with demand share up 3.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about predictive modeling
+Is predictive modeling in demand in 2026?
Yes. predictive modeling appears in 1,386 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Scientist accounts for the most postings mentioning predictive modeling (34.2% of all postings mentioning predictive modeling).
+What jobs require predictive modeling?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning predictive modeling are Principal Data Scientist (25.0% of that role’s postings mention predictive modeling), Marketing Analytics Director (22.2% of that role’s postings mention predictive modeling), Marketing Analytics Lead (19.0% of that role’s postings mention predictive modeling).
+What skills are commonly paired with predictive modeling?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), predictive modeling most often appears alongside Python, SQL, machine learning, R, data visualization.
+Where is predictive modeling most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring predictive modeling are New York City, San Francisco, London, Chicago, Barcelona, according to the Skillenai jobs index.
+How can I keep up with new predictive modeling content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning predictive modeling alongside the jobs index. You can subscribe to a daily email digest of new predictive modeling content from your Skillenai account.
+Which skills come before and after predictive modeling?
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 modeling — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around predictive modeling
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before predictive modeling
Skills after predictive modeling
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 | 28 |
| Before: sql | 24 |
| Before: Power BI | 16 |
| Before: Tableau | 13 |
| Before: Excel | 11 |
| Before: MYSQL | 8 |
| Before: ETL pipelines | 7 |
| Before: predictive models | 7 |
| After: sql | 10 |
| After: python | 8 |
| After: predictive analytics | 8 |
| After: sentiment analysis | 7 |
| After: Power BI | 7 |
| After: Azure Data Factory | 6 |
| After: Tableau | 6 |
| After: predictive models | 5 |
Roles most likely to require predictive modeling
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Principal Data Scientist | 10 | 25.0% |
| Marketing Analytics Director | 6 | 22.2% |
| Marketing Analytics Lead | 4 | 19.0% |
| AI Data Scientist | 3 | 15.0% |
| Autonomy Engineer | 3 | 15.0% |
| Data Science Manager | 32 | 14.0% |
| Lead Data Scientist | 13 | 13.0% |
| Data Analytics Director | 3 | 12.5% |
| Data Science Consultant | 9 | 12.2% |
| Machine Learning Manager | 3 | 12.0% |
Roles with the most predictive modeling postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 474 | 34.2% |
| Data Analyst | 84 | 6.1% |
| Data Science Manager | 32 | 2.3% |
| Product Manager | 24 | 1.7% |
| Machine Learning Engineer | 23 | 1.7% |
| Data Engineer | 18 | 1.3% |
| Lead Data Scientist | 13 | 0.9% |
| AI Engineer | 12 | 0.9% |
| Analyst | 12 | 0.9% |
| Business Analyst | 10 | 0.7% |
Top companies posting jobs requiring predictive modeling
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Accenture | 19 |
| Bah | 18 |
| Barclays | 15 |
| AbbVie | 15 |
| The Home Depot | 14 |
| Uvmhealth | 11 |
| Xometry | 11 |
| Cisco | 11 |
| BMO | 10 |
| 10 |
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 modeling
| Name | Postings | Share |
|---|---|---|
| New York City | 68 | 4.9% |
| San Francisco | 37 | 2.7% |
| London | 36 | 2.6% |
| Chicago | 32 | 2.3% |
| Barcelona | 21 | 1.5% |
| Toronto | 21 | 1.5% |
| Atlanta | 19 | 1.4% |
| Bengaluru | 15 | 1.1% |
| Seattle | 14 | 1.0% |
Skills commonly paired with predictive modeling
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. 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 modeling by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s predictive modeling postings by all predictive modeling 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: 0.5% to 0.6%. 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 2,596 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (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
- 812a167e1280c493
- data_as_of
- 2026-09-30
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
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