How scores are calculated

CareerStar treats each career like a financial asset — weighing expected return against risk — and blends the result with how well the path fits you. Everything below is deterministic: the same inputs always produce the same score. The number is computed by an explicit model, not by an AI language model.

The model

Each occupation gets a 0–100 score from three ingredients:

Return — projected employment growth and median pay, each ranked against every other occupation in the set.
Risk — AI/automation exposure (dominant) plus a volatility proxy (a field projected to shrink is treated as riskier).
Fit — the share of your stated interests the occupation matches.

Return = wGrowth·growth + wPay·pay
Risk   = wExposure·exposure + wVolatility·volatility
RAV    = Return · (1 − γ·Risk)          (risk-adjusted return)
Score  = 100 · [ α·RAV + (1 − α)·Fit ]

The weights (wGrowth, wPay, wExposure, wVolatility, γ, α) are explicit and documented in the code, so the model can be tuned and its sensitivity analyzed — raise the pay weight and finance climbs; raise γ and AI-exposed fields sink.

Data sources

Growth & pay— U.S. Bureau of Labor Statistics, Employment Projections 2024–2034 (public domain) — ~730 occupations across every field.
AI exposure — Eloundou et al. 2023, “GPTs are GPTs” (occupation-level exposure, β measure; MIT-licensed).
Occupations are keyed by O*NET-SOC code.

Limitations (read these)

Exposure is not job loss. AI exposure measures the share of tasks a model could touch — not whether the job disappears.
It’s an estimate, not a prediction. No one can see the future of the labor market; this is a grounded, transparent snapshot.
Fit is approximate.Interest tags are derived from each occupation’s category and title, not from O*NET’s detailed skill vectors — so “fit” is a rough signal, not a precise one.
The volatility term is a proxy. No public dataset measures career volatility directly, so it is constructed from projected growth.

Built with an AI-native workflow. The language model only writes the plain-English explanations — it never computes a score.