Compares each company's hiring mix (% of open reqs per P&L bucket) to filed opex mix from the latest 10-Q.
R&D ratio > 1.25 indicates engineering hiring outpaces filed R&D spend — a leading indicator of tech-pivot.
ratio > 1.25 = over-hiring R&D ·
0.75–1.25 = aligned ·
ratio < 0.75 = under-hiring R&D
Ticker
Sector
Period
Revenue
n reqs
Hiring R&D
Filed R&D
R&D ratio
Hiring S&M
Filed S&M
Hiring COGS
Filed COGS
Signal
opex.csv not found. Run python fetch_opex.py from the bundle root to populate.
Sector economics
Estimated total opex extrapolated from disclosed salaries across all open reqs.
Sector
Open reqs
Tickers
Coverage
Avg salary
Est. payroll
% of universe
Count vs dollar-weighted R&D mix
Positive Δ = company hires fewer engineers than headcount share implies, but they're expensive (senior-heavy).
Negative Δ = lots of engineers by count, but real dollars go to non-R&D functions.
Ticker
Sector
n
R&D count
R&D $
Δpp
Interpretation
AI/ML hiring intensity by company
Share of JDs mentioning LLM / GenAI / PyTorch / HuggingFace / foundation models / transformers.
Tickers with under 20 reqs excluded.
Ticker
Sector
AI reqs
Total
Share
Skill cluster intensity by sector
Share of sector's postings mentioning skills in each cluster.
Sector
Total reqs
AI/ML %
GPU %
Cloud %
Lifecycle data
Per-ticker lifecycle stats: days-to-close, closure rate, and posted-age medians. All computed from accumulated
daily runs. Values firm up after ~10 days of history.
Ticker
Open today
Tracked ever
Closed (lifetime)
Avg days open
Closure rate
Field coverage per ticker
Color code:
green ≥80% ·
amber 40–79% ·
red <40%
Ticker
n
Desc
Salary
Emp
Posted
Dept
Skills
Yrs
Select company
Choose a ticker to see its full daily posting history, P&L mix, top departments & skills, and searchable postings table.