Methodology

All scoring algorithms are documented here for transparency. We believe career intelligence tools must be explainable and auditable.

Job Risk Score

4-component composite measuring automation, wage, demand, and resilience signals.

Supply pressure from wage stagnation, projected openings, entry friction, and search trends.

Scenario-based outlook factoring demand, supply gap, AI disruption, and competition window.

Task-level classification of AI exposure with multi-source cross-validation.

5-year NPV with discount rate, entry ramp, transition probability, and 3 scenarios.

BLS OEWS — Occupational Employment and Wage Statistics. Annual release. Public domain.
BLS Employment Projections — 10-year scenario-based projections. Annual release.
BLS JOLTS — Job Openings and Labor Turnover Survey. Industry-level only. Monthly.
O*NET — Occupational Information Network. 923 occupations. CC BY 4.0.
Frey & Osborne (2013) — Automation probabilities for 702 occupations.
Webb AI Exposure (2020) — AI exposure via patent text analysis.
OpenAI GPT Exposure (2023) — Task-level LLM exposure scoring.

All forward-looking outputs are scenario-based outlooks, not predictions.
BLS projections historically have ~5.6% mean absolute error per year.
Automation research dates from 2013-2023 and may not reflect latest AI capabilities.
Salary data represents national medians; outcomes vary by location and experience.
Google Trends momentum is feature-flagged and may be unavailable.