logistic regression jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-30, logistic regression appears in 162 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning logistic regression, with demand share down 8.1% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-30
Which roles want logistic regression?
Upload your resume and Skillenai will show which roles your logistic regression experience fits, which skills you already cover, and what is missing.
Frequently asked questions about logistic regression
+Is logistic regression in demand in 2026?
Yes. logistic regression appears in 162 job postings indexed by Skillenai over the 90 days ending 2026-10-30. Data Scientist accounts for the most postings mentioning logistic regression (34.0% of all postings mentioning logistic regression).
+What jobs require logistic regression?
According to the Skillenai jobs index over the 90 days ending 2026-10-30, among roles with at least 20 postings, the highest shares mentioning logistic regression are Credit Risk Manager (16.0% of that role’s postings mention logistic regression), Quantitative Analyst (7.3% of that role’s postings mention logistic regression), Quant Developer (4.8% of that role’s postings mention logistic regression).
+What skills are commonly paired with logistic regression?
Across job postings indexed by Skillenai (90 days ending 2026-10-30), logistic regression most often appears alongside Python, SQL, R, machine learning, linear regression.
+Where is logistic regression most in demand?
As of 2026-10-30, the metro areas posting the most jobs requiring logistic regression are McLean, Pune, Bangkok, Bengaluru, Chicago, according to the Skillenai jobs index.
+How can I keep up with new logistic regression content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning logistic regression alongside the jobs index. You can subscribe to a daily email digest of new logistic regression content from your Skillenai account.
+Which skills come before and after logistic regression?
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 logistic regression — last 90 days
Career paths around logistic regression
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before logistic regression
Skills after logistic regression
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 | 74 |
| Before: sql | 56 |
| Before: Power BI | 30 |
| Before: Tableau | 30 |
| Before: Excel | 23 |
| Before: R | 17 |
| Before: xgboost | 12 |
| Before: pandas | 12 |
| After: python | 41 |
| After: sql | 40 |
| After: Tableau | 36 |
| After: Power BI | 24 |
| After: pandas | 21 |
| After: PySpark | 16 |
| After: docker | 14 |
| After: aws sagemaker | 13 |
Roles most likely to require logistic regression
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Credit Risk Manager | 4 | 16.0% |
| Quantitative Analyst | 13 | 7.3% |
| Quant Developer | 1 | 4.8% |
| Risk Analyst | 3 | 4.6% |
| Analytics Intern | 1 | 3.6% |
| Analytics Consultant | 4 | 3.4% |
| Product Analytics Manager | 1 | 3.2% |
| BI Analyst | 1 | 3.1% |
| Business Intelligence Director | 1 | 3.1% |
| Scientist | 1 | 1.6% |
Roles with the most logistic regression postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 55 | 34.0% |
| Quantitative Analyst | 13 | 8.0% |
| Machine Learning Engineer | 11 | 6.8% |
| Data Analyst | 8 | 4.9% |
| ML Engineer | 6 | 3.7% |
| Analytics Consultant | 4 | 2.5% |
| Credit Risk Manager | 4 | 2.5% |
| AI/ML Engineer | 3 | 1.9% |
| Risk Analyst | 3 | 1.9% |
| Software Engineer | 3 | 1.9% |
Top companies posting jobs requiring logistic regression
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Capital One | 13 |
| TransUnion | 6 |
| Mastercard | 5 |
| USAA | 5 |
| Experian | 5 |
| Agoda | 4 |
| NiCE | 4 |
| MTB | 4 |
| Novartis | 3 |
| Quantiphi | 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 logistic regression
| Name | Postings | Share |
|---|---|---|
| McLean | 9 | 5.6% |
| Pune | 5 | 3.1% |
| Bangkok | 4 | 2.5% |
| Bengaluru | 4 | 2.5% |
| Chicago | 4 | 2.5% |
| New York City | 4 | 2.5% |
| San Francisco | 4 | 2.5% |
| Singapore | 4 | 2.5% |
| Mumbai | 3 | 1.9% |
Skills commonly paired with logistic regression
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 2026-10-30. 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 logistic regression by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s logistic regression postings by all logistic regression 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
- 9a1fb06a440b06bf
- data_as_of
- 2026-10-30
- window_days
- 90