experimentation jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-11-04, experimentation appears in 3,616 job postings indexed by Skillenai over the past 90 days — Product Manager has the most postings mentioning experimentation, with demand share down 2.0% vs the prior 4 weeks.
Last updated · 90d ending 2026-11-04
Which roles want experimentation?
Upload your resume and Skillenai will show which roles your experimentation experience fits, which skills you already cover, and what is missing.
Frequently asked questions about experimentation
+Is experimentation in demand in 2026?
Yes. experimentation appears in 3,616 job postings indexed by Skillenai over the 90 days ending 2026-11-04. Product Manager accounts for the most postings mentioning experimentation (34.4% of all postings mentioning experimentation).
+What jobs require experimentation?
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 experimentation are Data Science Manager (25.4% of that role’s postings mention experimentation), Product Data Scientist (25.0% of that role’s postings mention experimentation), Growth Product Manager (24.0% of that role’s postings mention experimentation).
+What skills are commonly paired with experimentation?
Across job postings indexed by Skillenai (90 days ending 2026-11-04), experimentation most often appears alongside A/B testing, Python, SQL, machine learning, Product Management.
+Where is experimentation most in demand?
As of 2026-11-04, the metro areas posting the most jobs requiring experimentation are New York City, San Francisco, London, Toronto, Seattle, according to the Skillenai jobs index.
+How can I keep up with new experimentation content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning experimentation alongside the jobs index. You can subscribe to a daily email digest of new experimentation content from your Skillenai account.
+Which skills come before and after experimentation?
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 experimentation — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around experimentation
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before experimentation
Skills after experimentation
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: machine learning | 2 |
| Before: stand-ups | 1 |
| Before: DealRoom | 1 |
| Before: retrospectives | 1 |
| Before: RF environments | 1 |
| Before: environmental effects | 1 |
| Before: Subscription management | 1 |
| Before: Quality Assurance | 1 |
| After: MRI | 1 |
| After: mssql | 1 |
| After: C# | 1 |
| After: containers | 1 |
| After: ETL | 1 |
| After: optimization | 1 |
| After: .NET Core | 1 |
| After: SQL tables | 1 |
Roles most likely to require experimentation
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Data Science Manager | 57 | 25.4% |
| Product Data Scientist | 10 | 25.0% |
| Growth Product Manager | 12 | 24.0% |
| Product Analytics Manager | 7 | 21.9% |
| Marketing Analytics Director | 5 | 21.7% |
| Machine Learning Manager | 5 | 20.8% |
| Machine Learning Engineering Manager | 10 | 16.4% |
| Lead Product Manager | 10 | 16.1% |
| Group Product Manager | 16 | 14.7% |
| Product Analyst | 30 | 14.6% |
Roles with the most experimentation postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Product Manager | 1,245 | 34.4% |
| Data Scientist | 463 | 12.8% |
| Product Designer | 156 | 4.3% |
| AI Engineer | 136 | 3.8% |
| Software Engineer | 134 | 3.7% |
| Machine Learning Engineer | 100 | 2.8% |
| Engineering Manager | 92 | 2.5% |
| Data Analyst | 58 | 1.6% |
| Data Science Manager | 57 | 1.6% |
| Product Owner | 34 | 0.9% |
Top companies posting jobs requiring experimentation
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Bjakcareer | 157 |
| Capital One | 131 |
| Stripe | 63 |
| Expedia Group | 55 |
| 51 | |
| 45 | |
| Airbnb | 41 |
| Spotify | 38 |
| Uber | 35 |
| Monzo | 34 |
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 experimentation
| Name | Postings | Share |
|---|---|---|
| New York City | 242 | 6.7% |
| San Francisco | 211 | 5.8% |
| London | 157 | 4.3% |
| Toronto | 88 | 2.4% |
| Seattle | 72 | 2.0% |
| Singapore | 49 | 1.4% |
| Amsterdam | 41 | 1.1% |
| Berlin | 41 | 1.1% |
| Boston | 41 | 1.1% |
Skills commonly paired with experimentation
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 experimentation by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s experimentation postings by all experimentation 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: 1.7% to 1.6%. 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 2,472 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
- 7c648c3713ae6141
- data_as_of
- 2026-11-04
- window_days
- 90