Hiring is the earliest signal a company sends. Before earnings calls, before press releases, before the 10-K — companies tell you what they're going to do by who they hire. Hirebase turns 300k+ public career pages into a clean, deduplicated, point-in-time-accurate hiring dataset built for quantitative funds, fundamental analysts, and data desks.
hirebase — alt-data terminal
> # hiring_velocity_60d --industry "fintech" --rank 5
> # role_flow --signal "AI infra" --emergent
function: "Senior MLE – GPU infra"
+312 opened, 30dfunction: "Forward Deployed Eng"
+87 opened, 30dfunction: "Applied Researcher"
+64 opened, 30dEvery competitor pivot, every new market, every product roadmap shift leaks first into job postings. By the time a strategic move hits TechCrunch, it's been encoded in their hiring patterns for months. Here's what to watch — and what each pattern means.
First-seen and last-seen timestamps on every role mean you can compute exact open-role flow, net opens minus closures, and rolling 30/60/90-day velocity per company, sector, or function.
Detect new market entry months ahead of announcements. Geocoded role data shows which cities, countries, and regions are getting investment — and which are being quietly wound down.
A surge in "Forward Deployed Engineer" roles signals enterprise focus. A wave of "Applied Research" hires signals AI investment. Function mix is the leading indicator of strategy shifts.
AI-extracted skills and tools across millions of postings. Watch enterprise adoption curves of any specific technology — Snowflake, Databricks, Kubernetes, OpenAI APIs — at the company level.
A drop in active postings, combined with role-removal events tracked in the historical archive, surfaces hiring freezes weeks before they’re announced or RIF disclosures hit.
Millions of posted salary points across roles, levels, and geographies. Track compensation inflation in real time — by industry, function, or specific employer — months ahead of official statistics.
Work at the level that fits your research. Pull individual records for backtesting, query pre-computed signals through the Insights API, or filter at the company level for portfolio screening and lead generation.
Pull raw, enriched, point-in-time job records with first-seen and last-seen timestamps. The atomic unit. Best for building your own factors and running custom aggregations against the historical archive.
Pre-computed hiring velocity, salary trends, function-mix shifts, and geographic flow signals queryable per company, sector, or universe. Skip the aggregation step and pull the signal directly into your model.
Query 300,000 enriched company profiles by industry, services, technology stack, geography, and hiring activity. Identify companies entering new markets, scaling specific functions, or matching your investment criteria without processing individual job records.
For quantitative funds, fundamental analysts, and corporate research desks. Hirebase delivers a clean, deduplicated hiring dataset with full historical depth back to 2023, structured for direct ingestion into your research stack.
Point-in-time accurate. Every record carries first-seen and last-seen timestamps, to the second. No retroactive rewrites. Backtest against the data as it actually existed on any given date.
Hiring velocity, ready to compute. Because we capture exact post and removal timestamps, you can derive role-flow velocity at any window — 7d, 30d, 60d, 90d — without reconstructing the data yourself.
Direct from source. Sourced from public company career pages and ATS platforms — not aggregator feeds. Cleaner, fresher, and free from the duplication that pollutes other vendors' datasets.
Entity-resolved. Every job links to a canonical company entity, ticker-mapped where the company is public. Industry codes, ATS-platform metadata, and geographic data join cleanly to your existing universe.
Delivered how you want it. REST API for live access, S3 drops in CSV / Parquet / JSON for daily syncs, or one-time CSV/Parquet exports for the full history. Sample data available under NDA.
Most "hiring data" providers resell aggregator feeds. We don't. Here's how we build, clean, and serve a dataset that holds up to a quant team's diligence.
300k+ public company career pages and 80+ ATS platforms (Greenhouse, Lever, Workday, Ashby, iCIMS, SmartRecruiters, and more), scanned continuously — new jobs within 1–2 hours, removals within the hour per day. No third-party aggregator feeds. No reposts. No staffing-firm noise.
Every record is timestamped on first observation, to the second, and never retroactively edited. When a posting is removed from the source, we record that as a separate event with its own timestamp — and surface those removals through a dedicated expired jobs feed so you can run daily delta syncs and track role closures precisely.
Multi-stage deduplication: URL canonicalization, title normalization, JD content hashing, and company-resolved entity matching. The same listing across multiple ATS platforms collapses into a single canonical record — so velocity calculations and role-flow signals don't double-count.
Every company is canonicalized to a unique entity with industry codes, geography, ATS platform, and — where the company is public — a mapped ticker. Join-ready against your existing security master.
AI-extracted seniority, function, salary range, tech stack, benefits, visa status, education requirement, location with geocode. The data is research-ready, not raw text.
All records originate from publicly accessible career pages. We respect robots.txt directives and rate-limit responsibly. Full DDQ available covering data licensing, sourcing methodology, and compliance documentation. Listed on Neudata →
Whether you're prototyping a signal in a Jupyter notebook or piping daily deltas into your warehouse, we deliver in the format that fits your stack.
A side-by-side look at how Hirebase compares to aggregator-derived job datasets and legacy alt-data vendors. The differences compound over time.
Stop briefing leadership with quarterly Gartner reports and slide decks built last sprint. Hirebase data flows into your existing BI stack — Looker, Tableau, Hex, Metabase, custom internal tools — and updates as the market does.
Compute role-flow velocity, function-mix shifts, and geographic expansion per ticker from point-in-time postings. Backtest against the archive as it existed on any date, then pull daily deltas into your factor pipeline.
Watch a coverage name's hiring by function, seniority, and city ahead of the print. Net opens minus removals and posted comp trends give you evidence management hasn't disclosed yet.
Diligence a target's real headcount plan from its postings: which functions are scaling, where, and at what posted pay. Track portfolio companies and emerging entrants in a category without asking founders for a deck.
Aggregate posting volume, posted salaries, and remote share by industry and geography to nowcast labor demand weeks before official statistics land.
Seed a labor-market product with the full historical archive, then keep it current with daily export deltas and the expired-jobs feed. Entity-resolved and deduplicated before it reaches you.
Benchmark your own hiring plan and posted comp against a competitive set, and brief leadership with evidence from the postings themselves instead of analyst-firm summaries.
We've been through DDQ with hedge funds, PE firms, and corporate procurement. Here's where we stand on the questions you'll be asked to answer internally before signing.
Sample data and full DDQ available on request under standard NDA. Also listed on Neudata for fund evaluators already running diligence through the platform.
Request full DDQ
Request a sample dataset, run your own analysis, and decide whether the signal fits your process.
