WFH.team

WFH.team — A daily-snapshotted remote job-market panel plus adjacent remote-work research series, generated by the site's own job inventory and candidate-matching product. The Remote Job Market dataset publishes, per dated release: active and new remote job counts (7,129 active / 5,884 new in the latest 30-day window), salary-transparency rate (50.2% of active listings carry salary) and median listed salary (146,562 USD), a 19-occupation breakdown carrying new jobs, active jobs, applications, candidate demand and a jobs-per-100-candidates intensity ratio (e.g. Software 5,453 active with 2,485…

WFH.team offers (Alternative, Sentiment) — Stated at panel level: 7,129 active and 5,884 new remote jobs in the latest 30-day window, tracked daily, with 19 occupation classes, 12+ countries, timezone, 6 seniority and 4 restriction classes and salary bands; candidate-demand counts run from 13 to 4,621 per occupation. Bounded estimate: the inventory is a rolling remote-only subset rather than a whole-market index, so cumulative history is the asset — order 10^5-10^6 listing-observations if the daily snapshot has run for 1-2 years, each carrying classification labels, plus per-occupation application/candidate aggregates, a weekly scam-risk series and a quarterly survey panel. Exact history length and raw-listing corpus size are not published..

Remote wage benchmarking and salary-transparency tracking (median listed salary and the transparency rate as time series, plus salary bands); labour-scarcity and applicant-flow measurement by occupation via jobs per 100 candidates — directly usable in compensation and hiring-difficulty models and rare because it needs both sides of the market; remote-work structural-shift monitoring (the geographic-restriction mix is a leading indicator of return-to-office policy and of US-only versus global hiring); location-strategy and employer-of-record planning from country and timezone distributions; skills-demand tracking; hiring-difficulty and pay-pressure factors for equity research on remote-heavy software and work-tools names; fraud/abuse model training and marketplace trust scoring from the Job Scam Radar warning-signal series; NLP training data for remote-eligibility and geographic-restriction classification from listing wording; and worker-preference survey data for workforce-experience research.