Transitions · preview

Career transitions.

A DOORS-inspired decision-support tool for job seekers. Given your current role and skills, what adjacent careers are worth considering — by earnings, by skill similarity, and by demand in your area? Same shape as the business surface: structured synthesis, two reads, explained gap. The product is a reading aid, not a recommendation engine.

Preview · not shipped · backend in development

Why this domain

Career transitions are exactly the kind of decision where a synthesis layer earns its keep. The noise-to-signal ratio in career media is high — listicles, anecdotal advice, sponsored content — and there's no liquid prediction market to defer to. The structured signals that exist (O*NET skill vectors, BLS wages, posting volume) are public but scattered, and rarely combined in a way that's legible to an individual making a real choice.

The research question scales cleanly from the business surface: can structured synthesis materially improve a job seeker's decision quality, relative to unaided search? Decision quality here is calibration of the seeker's confidence, regret after the move, and time-to-clarity. Forecast accuracy is not the metric. Match quality is.

How it will work

  1. 01Intake

    Current role (O*NET SOC code or free text), education level, skills, ZIP, and your goal — earnings, similarity, or availability. Resume upload optional.

  2. 02Score

    A pure scorer ranks candidate occupations by a weighted blend of earnings (BLS OES median wage), skill-vector similarity (O*NET), and demand (CareerOneStop posting volume). Outputs are z-scored against a sender-scoped candidate set.

  3. 03Advise

    An LLM reads the top-ranked candidates, the intake, and the scorer rationale, and writes a structured recommendation: which transition is most defensible and why, drawing only from the data the scorer saw.

  4. 04Decide

    You see the ranked list, the rationale, and the underlying signal — wage, similarity, demand — broken out per option. The product is the structured read; the decision is yours.

Sources

Public reference data, all already ingested into the system. None of it is proprietary; the value is in the synthesis.

  • O*NETOccupation taxonomy, skill importance vectors, typical education, STEM flag.
  • BLS OESMedian annual wage and employment count per SOC code, refreshed annually.
  • BLS Employment Projections10-year projected growth per SOC, used as an optional weight.
  • CareerOneStopPosting count and sample postings cached nightly per SOC.

What's shipped, what isn't

Shipped

  • Reference data tables and ingestion jobs (O*NET, OES, BLS, CareerOneStop).
  • Scorer (server/careers/score.js) — pure, deterministic, tested.
  • Recommender orchestrator and LLM advisor (server/careers/recommend.js, advisor/advise.js).
  • Intake / recommendations / snapshot schema with RLS policies.

Not yet

  • Public API endpoints for intake submission and recommendation retrieval.
  • Intake form, candidate-list rendering, advisor rationale UI.
  • Pilot cohort onboarding (Albert Qian's network).
  • Snapshot share URLs (parallel to the business surface's score snapshots).

Pilot cohort

Initial validation is planned with a small pilot group rather than a public launch. If you're a job seeker actively considering a transition and want to be on the pilot list, write to [email protected] — happy to share the framing in more detail.

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