Career Dish Career decisions, not job descriptions

Methodology

How Career Dish turns career data into decisions.

Career Dish is built as a reference tool for people deciding whether to pursue a career. We use public labor datasets for the baseline, then add editorial analysis to explain the lived tradeoffs that raw data does not answer by itself.

Purpose

The question each guide answers is not "what does this occupation do?" The question is "should this person spend time, money, attention, and identity on this career path?"

That is why Career Dish pages include pay, education cost, licensing or proof requirements, stress, emotional labor, physical labor, analytical load, AI exposure, alternatives, and who should avoid the career.

Primary data sources

  • O*NET Database 30.3 for occupation records, work context, work activities, job zones, education signals, and alternate titles.
  • BLS Occupational Employment and Wage Statistics, May 2025 national estimates, for pay baselines.
  • BLS Employment Projections 2024 to 2034 for outlook, annual openings, education, experience, and training context where available.

How a guide is built

1

Start with the occupation record

We match the career to the closest public occupation record, then pull pay, outlook, education, work-context, and work-activity signals.

2

Translate work signals into decision metrics

Raw occupational data is turned into reader-facing metrics such as social load, analytical load, emotional labor, physical labor, routine, autonomy, urgency, precision, and AI exposure.

3

Add editorial analysis

Each priority career gets a specific point of view about what the job really rewards, what people misunderstand, what makes it stressful, and what kind of person should avoid it.

4

Build support pages only where the query deserves it

Salary, stress, day-in-the-life, career-change, alternatives, and comparison pages exist when they answer a distinct decision question.

How AI is used

AI is not the product. It is a production aid used to help organize source material, draft structured explanations, and convert data into readable decision frameworks. The product is the combination of public data, original scoring, editorial judgment, and page structure.

Interview-style sections on Career Dish are explanatory devices. They are designed to make a job's day-to-day tradeoffs easier to scan and understand. They should not be read as literal interviews with named real people unless a page explicitly says otherwise.

Limits

  • National pay and outlook data can hide local differences by state, employer, union, specialty, seniority, and economic cycle.
  • Some modern careers do not map cleanly to one BLS or O*NET occupation, so the guide notes when a public-data proxy is being used.
  • AI exposure scores are work-signal estimates, not predictions that a job will disappear.
  • Career Dish is not career counseling, legal advice, financial advice, or a guarantee of employment outcomes.

What changed in the re-release

Career Dish has moved away from being a set of article-style career stories. The new site architecture is a data-backed career decision directory with canonical career profiles, support pages for specific decision queries, a crawlable career directory, a methodology page, and clearer internal linking around the value the site actually delivers.