time-series analysis jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-04, time-series analysis appears in 303 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning time-series analysis, with demand share up 38.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-04
Which roles want Time Series analysis?
Upload your resume and Skillenai will show which roles your Time Series analysis experience fits, which skills you already cover, and what is missing.
Frequently asked questions about time-series analysis
+Is time-series analysis in demand in 2026?
Yes. time-series analysis appears in 303 job postings indexed by Skillenai over the 90 days ending 2026-11-04. Data Scientist accounts for the most postings mentioning time-series analysis (26.1% of all postings mentioning time-series analysis).
+What jobs require time-series analysis?
According to the Skillenai jobs index over the 90 days ending 2026-11-04, among roles with at least 20 postings, the highest shares mentioning time-series analysis are Quantitative Research Intern (26.1% of that role’s postings mention time-series analysis), Quantitative Analyst (6.9% of that role’s postings mention time-series analysis), Quantitative Researcher (6.6% of that role’s postings mention time-series analysis).
+What skills are commonly paired with time-series analysis?
Across job postings indexed by Skillenai (90 days ending 2026-11-04), time-series analysis most often appears alongside Python, Machine Learning, SQL, PyTorch, R.
+Where is time-series analysis most in demand?
As of 2026-11-04, the metro areas posting the most jobs requiring time-series analysis are New York City, London, McLean, San Francisco, Boston, according to the Skillenai jobs index.
+How can I keep up with new time-series analysis content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning time-series analysis alongside the jobs index. You can subscribe to a daily email digest of new time-series analysis content from your Skillenai account.
+Which skills come before and after time-series analysis?
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 time-series analysis — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around time-series analysis
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before time-series analysis
Skills after time-series analysis
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 | 6 |
| Before: sql | 4 |
| Before: Tableau | 4 |
| Before: MYSQL | 2 |
| Before: Power BI | 2 |
| Before: sentiment analysis | 2 |
| Before: regression analysis | 2 |
| Before: data integrity | 2 |
| After: scikit-learn | 5 |
| After: pytorch | 4 |
| After: databricks | 3 |
| After: xgboost | 3 |
| After: tensorflow | 3 |
| After: exploratory data analysis | 3 |
| After: Tableau | 3 |
| After: R | 2 |
Roles most likely to require time-series analysis
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Quantitative Research Intern | 6 | 26.1% |
| Quantitative Analyst | 13 | 6.9% |
| Quantitative Researcher | 10 | 6.6% |
| Applied AI Scientist | 2 | 6.2% |
| Bioinformatics Scientist | 1 | 5.0% |
| Quantitative Developer | 5 | 5.0% |
| Lead Architect | 1 | 4.8% |
| Quant Developer | 1 | 4.8% |
| AI/ML Scientist | 1 | 4.5% |
| Credit Risk Manager | 1 | 3.8% |
Roles with the most time-series analysis postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 79 | 26.1% |
| Software Engineer | 18 | 5.9% |
| ML Engineer | 13 | 4.3% |
| Quantitative Analyst | 13 | 4.3% |
| Data Analyst | 11 | 3.6% |
| AI Engineer | 10 | 3.3% |
| Quantitative Researcher | 10 | 3.3% |
| Machine Learning Engineer | 8 | 2.6% |
| Quantitative Research Intern | 6 | 2.0% |
| Engineering Manager | 5 | 1.7% |
Top companies posting jobs requiring time-series analysis
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| General Motors | 16 |
| Capital One | 13 |
| GeVernova | 9 |
| Monolith | 5 |
| Trading | 5 |
| Renesas | 4 |
| 4 | |
| True Anomaly | 4 |
| Schonfeld | 4 |
| Planetlabs | 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 time-series analysis
| Name | Postings | Share |
|---|---|---|
| New York City | 15 | 5.0% |
| London | 10 | 3.3% |
| McLean | 9 | 3.0% |
| San Francisco | 8 | 2.6% |
| Boston | 7 | 2.3% |
| Chennai | 7 | 2.3% |
| Hong Kong | 5 | 1.7% |
| Washington | 5 | 1.7% |
| Bellevue | 4 | 1.3% |
Skills commonly paired with time-series analysis
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 2026-11-04. 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 time-series analysis by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s time-series analysis postings by all time-series analysis 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 2,472 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
- 0c377658df7d677d
- data_as_of
- 2026-11-04
- window_days
- 90