Astronomers AI displacement risk

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

17 O*NET task statements matched to SOC 19-2011. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $128,820 (May 2025, US national). The latest BLS row matched SOC 19-2011.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

What the AI risk score means for Astronomers

The displacement pressure score for Astronomers is 24. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Analyze research data using computers carries 36% automation pressure, while Analyze research data using computers carries 74% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $128,820 (May 2025, US national). Employment context: Small research field riding the survey-data deluge. Typical education: Doctoral degree required.

Wage vulnerability is 22, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

• Low displacement pressure
• ML pipelines are standard tooling
• Grant competition keeps publication human

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
2. By 60 days, complete one small project connected to Data Scientist, such as productize ml pipelines.
3. By 90 days, compare internal openings and external postings for Data Scientist or Observatory Instrument Scientist and update your resume around measurable workflow outcomes.