DEEPCOMPS · ABOUT

About DeepComps

A labor-market analysis lab that publishes the math behind compensation and career decisions — sourced from BLS, BEA, O*NET, and professional licensing boards. No self-report aggregation, no career-coach opinions, no opaque salary "estimates."

What we publish

Three things, all backed by federal datasets:

  • Real-wage tables — BLS Occupational Employment Statistics divided by BEA Regional Price Parity, so the same $200K nominal offer can be honestly compared as $178K real in California (RPP 112.2) versus $206K real in Texas (RPP 97.1) versus $230K real in Mississippi (RPP 86.8).
  • License reciprocity matrices — current member states, pending bills, endorsement steps, and primary-state-of-residence rules for nursing, physical therapy, real estate, and teaching.
  • Career transition ROI — O*NET skill-gap math + program tuition + multi-year NPV. Every assumption editable.

What we don't do

  • Aggregate self-reported salary data (selection bias is severe and well-documented).
  • Hand out "career advice" — we publish numbers and frameworks; you decide.
  • Run sponsored content. Affiliate links, where present, are clearly labeled and don't influence rankings or formulas.

Editorial principles

  1. Show your work. Every calculator publishes its formula. Every page links to its underlying data source.
  2. Federal first. BLS, BEA, O*NET, and professional-board data take precedence over crowd-sourced datasets.
  3. Update visibly. "Last synced" timestamp on every data-driven page. Changelog for monthly diffs.
  4. Acknowledge uncertainty. When a dataset is thin or proxied, we say so on the page rather than masking it.

Who runs this

DeepComps was founded in 2026 by Marcus Liang as an independent, one-person labor-market analysis project. Marcus owns the entire stack — data ingestion (BLS OEWS, BEA RPP, IRS Rev. Proc., state DOR brackets, NCSBN compact tracker), the per-state tax-computation engine, the real-wage rankings, and the editorial voice on every hub.

Why a one-person project? Most consumer salary tools either (1) rebrand self-reported survey data with selection bias baked in, or (2) bury federal data under sponsored content. DeepComps was built to prove the alternative was tractable: federal data, transparent formulae, no sponsorship, no career advice.

What DeepComps is not: not a recruiter, exam-prep vendor, bootcamp, certification provider, or career-coaching service. Those businesses have legitimate reasons to bias salary or transition figures — and DeepComps does not share their incentives. There are no investors, no sponsors, and no paid placements on any data-driven page. Affiliate links, where they appear on a small number of transition pages, are inline-disclosed.

Read the full editor profile and disclosures at /editorial-team/.

Contact

Editorial corrections, data-source suggestions, and partnership inquiries: [email protected].