Experimentation Frameworks jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, Experimentation Frameworks appears in 221 job postings indexed by Skillenai over the past 90 days — Product Manager has the most postings mentioning Experimentation Frameworks, with demand share down 16.0% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want Experimentation Frameworks?
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Frequently asked questions about Experimentation Frameworks
+Is Experimentation Frameworks in demand in 2026?
Yes. Experimentation Frameworks appears in 221 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Product Manager accounts for the most postings mentioning Experimentation Frameworks (19.9% of all postings mentioning Experimentation Frameworks).
+What jobs require Experimentation Frameworks?
According to the Skillenai jobs index over the 90 days ending 2026-10-29, among roles with at least 20 postings, the highest shares mentioning Experimentation Frameworks are Principal Data Scientist (7.5% of that role’s postings mention Experimentation Frameworks), Product Analytics Manager (6.5% of that role’s postings mention Experimentation Frameworks), Product Director (4.9% of that role’s postings mention Experimentation Frameworks).
+What skills are commonly paired with Experimentation Frameworks?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), Experimentation Frameworks most often appears alongside A/B testing, Python, SQL, machine learning, data pipelines.
+Where is Experimentation Frameworks most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring Experimentation Frameworks are San Francisco, New York City, San Jose, London, Boston, according to the Skillenai jobs index.
+How can I keep up with new Experimentation Frameworks content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Experimentation Frameworks alongside the jobs index. You can subscribe to a daily email digest of new Experimentation Frameworks content from your Skillenai account.
+Which skills come before and after Experimentation Frameworks?
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 Frameworks — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Experimentation Frameworks
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Experimentation Frameworks
Skills after Experimentation Frameworks
No published outgoing skill pairs yet.
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: behavioral patterns | 1 |
| Before: multi-touch attribution models | 1 |
| Before: automate reporting pipeline | 1 |
| Before: data infrastructure | 1 |
| Before: measurement strategy | 1 |
| Before: cluster analysis | 1 |
| Before: Market Basket Analysis | 1 |
| Before: predictive modeling | 1 |
Roles most likely to require Experimentation Frameworks
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Principal Data Scientist | 3 | 7.5% |
| Product Analytics Manager | 2 | 6.5% |
| Product Director | 2 | 4.9% |
| Decision Scientist | 2 | 4.8% |
| Growth Product Manager | 2 | 4.0% |
| Growth Engineer | 2 | 3.7% |
| Marketing Operations Manager | 1 | 2.8% |
| Data Science Manager | 5 | 2.3% |
| AI Product Manager | 4 | 2.1% |
| GTM Engineer | 3 | 1.7% |
Roles with the most Experimentation Frameworks postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Product Manager | 44 | 19.9% |
| Data Scientist | 30 | 13.6% |
| Software Engineer | 20 | 9.0% |
| Machine Learning Engineer | 19 | 8.6% |
| Product Designer | 9 | 4.1% |
| Data Analyst | 8 | 3.6% |
| Engineering Manager | 7 | 3.2% |
| Data Science Manager | 5 | 2.3% |
| AI Product Manager | 4 | 1.8% |
| Data Engineer | 3 | 1.4% |
Top companies posting jobs requiring Experimentation Frameworks
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Adobe | 8 |
| PadSplit | 6 |
| Stripe | 5 |
| Roku | 5 |
| Disney | 4 |
| Elsevier | 4 |
| Ebury | 4 |
| CLERA | 4 |
| KoBold Metals | 4 |
| PRODIGAL | 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 Experimentation Frameworks
| Name | Postings | Share |
|---|---|---|
| San Francisco | 21 | 9.5% |
| New York City | 12 | 5.4% |
| San Jose | 10 | 4.5% |
| London | 7 | 3.2% |
| Boston | 6 | 2.7% |
| Seattle | 6 | 2.7% |
| Atlanta | 5 | 2.3% |
| Austin | 5 | 2.3% |
| Bengaluru | 5 | 2.3% |
Skills commonly paired with Experimentation Frameworks
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 2026-10-29. 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 Frameworks by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Experimentation Frameworks postings by all Experimentation Frameworks 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
- cfd55d4b862439b8
- data_as_of
- 2026-10-29
- window_days
- 90