Experimental design jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-29, Experimental design appears in 673 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning Experimental design, with demand share up 5.8% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-29
Which roles want Experimental design?
Upload your resume and Skillenai will show which roles your Experimental design experience fits, which skills you already cover, and what is missing.
Frequently asked questions about Experimental design
+Is Experimental design in demand in 2026?
Yes. Experimental design appears in 673 job postings indexed by Skillenai over the 90 days ending 2026-10-29. Data Scientist accounts for the most postings mentioning Experimental design (33.4% of all postings mentioning Experimental design).
+What jobs require Experimental design?
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 Experimental design are Deep Learning Engineer (10.0% of that role’s postings mention Experimental design), Product Data Scientist (9.5% of that role’s postings mention Experimental design), Applied Data Scientist (7.4% of that role’s postings mention Experimental design).
+What skills are commonly paired with Experimental design?
Across job postings indexed by Skillenai (90 days ending 2026-10-29), Experimental design most often appears alongside Python, SQL, machine learning, causal inference, A/B testing.
+Where is Experimental design most in demand?
As of 2026-10-29, the metro areas posting the most jobs requiring Experimental design are San Francisco, New York City, London, Seattle, Singapore, according to the Skillenai jobs index.
+How can I keep up with new Experimental design content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning Experimental design alongside the jobs index. You can subscribe to a daily email digest of new Experimental design content from your Skillenai account.
+Which skills come before and after Experimental design?
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 Experimental design — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around Experimental design
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before Experimental design
Skills after Experimental design
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 | 2 |
| Before: Tableau | 2 |
| Before: collaborative filtering | 2 |
| Before: R | 2 |
| Before: spark | 1 |
| Before: Embryonic stem cells | 1 |
| Before: K-means clustering | 1 |
| Before: NLP | 1 |
| After: machine learning | 4 |
| After: statistical analysis | 3 |
| After: python | 3 |
| After: Natural Language Processing | 2 |
| After: SQL Server | 2 |
| After: Data Science | 2 |
| After: Apache Spark | 2 |
| After: Azure Data Factory | 2 |
Roles most likely to require Experimental design
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Deep Learning Engineer | 3 | 10.0% |
| Product Data Scientist | 4 | 9.5% |
| Applied Data Scientist | 2 | 7.4% |
| Decision Scientist | 3 | 7.1% |
| Marketing Data Analyst | 4 | 6.2% |
| Applied Scientist | 15 | 6.1% |
| ML Scientist | 2 | 5.4% |
| Research Scientist | 55 | 5.3% |
| Principal Data Scientist | 2 | 5.0% |
| Research Fellow | 1 | 5.0% |
Roles with the most Experimental design postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 225 | 33.4% |
| Research Scientist | 55 | 8.2% |
| Machine Learning Engineer | 26 | 3.9% |
| Software Engineer | 23 | 3.4% |
| Data Analyst | 22 | 3.3% |
| Applied Scientist | 15 | 2.2% |
| Research Engineer | 12 | 1.8% |
| Analytics Manager | 11 | 1.6% |
| ML Engineer | 9 | 1.3% |
| Data Science Manager | 8 | 1.2% |
Top companies posting jobs requiring Experimental design
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| 17 | |
| Anthropic | 12 |
| General Motors | 12 |
| Uber | 11 |
| Grab | 10 |
| Truelogic | 9 |
| Wise | 9 |
| Booking.com | 8 |
| Mercor | 8 |
| Wikimedia Foundation | 7 |
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 Experimental design
| Name | Postings | Share |
|---|---|---|
| San Francisco | 50 | 7.4% |
| New York City | 41 | 6.1% |
| London | 26 | 3.9% |
| Seattle | 19 | 2.8% |
| Singapore | 16 | 2.4% |
| Toronto | 14 | 2.1% |
| Barcelona | 11 | 1.6% |
| Amsterdam | 10 | 1.5% |
| Boston | 10 | 1.5% |
Skills commonly paired with Experimental design
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 Experimental design by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Experimental design postings by all Experimental design 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: 0.3% to 0.3%. 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
- a9bfd2c2d02dc498
- data_as_of
- 2026-10-29
- window_days
- 90