recommendations jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-22, recommendations appears in 164 job postings indexed by Skillenai over the past 90 days — Product Manager has the most postings mentioning recommendations, with demand share down 10.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-22
Which roles want recommendations?
Upload your resume and Skillenai will show which roles your recommendations experience fits, which skills you already cover, and what is missing.
Frequently asked questions about recommendations
+Is recommendations in demand in 2026?
Yes. recommendations appears in 164 job postings indexed by Skillenai over the 90 days ending 2026-10-22. Product Manager accounts for the most postings mentioning recommendations (31.7% of all postings mentioning recommendations).
+What jobs require recommendations?
According to the Skillenai jobs index over the 90 days ending 2026-10-22, among roles with at least 20 postings, the highest shares mentioning recommendations are Engineering Product Manager (5.6% of that role’s postings mention recommendations), AI Product Designer (4.8% of that role’s postings mention recommendations), Enterprise Architect (3.8% of that role’s postings mention recommendations).
+What skills are commonly paired with recommendations?
Across job postings indexed by Skillenai (90 days ending 2026-10-22), recommendations most often appears alongside machine learning, ranking, search, personalization, A/B testing.
+Where is recommendations most in demand?
As of 2026-10-22, the metro areas posting the most jobs requiring recommendations are San Francisco, New York City, Amsterdam, Austin, Berlin, according to the Skillenai jobs index.
+How can I keep up with new recommendations content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning recommendations alongside the jobs index. You can subscribe to a daily email digest of new recommendations content from your Skillenai account.
+Which skills come before and after recommendations?
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 recommendations — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around recommendations
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before recommendations
Skills after recommendations
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 | 3 |
| Before: Tableau | 2 |
| Before: upgrades | 1 |
| Before: Revit | 1 |
| Before: Tableau Server | 1 |
| Before: engineering design | 1 |
| Before: data quality checks | 1 |
| Before: DCT | 1 |
| After: data analysis | 2 |
| After: Data | 2 |
| After: sql | 2 |
| After: pleadings | 1 |
| After: software supply chain security | 1 |
| After: suitability procedural interviews | 1 |
| After: Hive queries | 1 |
| After: fraudulent site takedowns | 1 |
Roles most likely to require recommendations
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Engineering Product Manager | 2 | 5.6% |
| AI Product Designer | 1 | 4.8% |
| Enterprise Architect | 20 | 3.8% |
| Product Data Scientist | 1 | 2.1% |
| Technology Architect | 1 | 1.9% |
| Machine Learning Engineer | 23 | 1.0% |
| Principal Product Manager | 2 | 0.9% |
| Machine Learning Scientist | 1 | 0.7% |
| Principal Software Engineer | 1 | 0.7% |
| Engineering Director | 1 | 0.5% |
Roles with the most recommendations postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Product Manager | 52 | 31.7% |
| Machine Learning Engineer | 23 | 14.0% |
| Enterprise Architect | 20 | 12.2% |
| Software Engineer | 12 | 7.3% |
| Engineering Manager | 9 | 5.5% |
| Data Scientist | 6 | 3.7% |
| Backend Engineer | 4 | 2.4% |
| AI Engineer | 3 | 1.8% |
| Product Designer | 3 | 1.8% |
| Product Owner | 3 | 1.8% |
Top companies posting jobs requiring recommendations
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Adobe | 15 |
| Frame.io | 9 |
| Snap | 5 |
| Stripe | 4 |
| Mozilla | 4 |
| 4 | |
| Snapchat | 4 |
| Constructor | 3 |
| Expedia Group | 3 |
| Etsy | 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 recommendations
| Name | Postings | Share |
|---|---|---|
| San Francisco | 13 | 7.9% |
| New York City | 7 | 4.3% |
| Amsterdam | 4 | 2.4% |
| Austin | 4 | 2.4% |
| Berlin | 4 | 2.4% |
| London | 4 | 2.4% |
| Toronto | 4 | 2.4% |
| San Jose | 3 | 1.8% |
| Bengaluru | 2 | 1.2% |
Skills commonly paired with recommendations
Explore related pages
How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-22. 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 recommendations by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s recommendations postings by all recommendations 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 2,610 identified employers and covers 72% of earlier and 73% of latest indexed postings. Windows: 2026-06-23 to 2026-09-21 and 2026-07-21 to 2026-10-19 (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
- fb2c6e5492dce021
- data_as_of
- 2026-10-22
- window_days
- 90