Statistical Assistants 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.

16 O*NET task statements matched to SOC 43-9111. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $50,330 (May 2025, US national). The latest BLS row matched SOC 43-9111.

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 Statistical Assistants

The displacement pressure score for Statistical Assistants is 76. 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. Enter and code data for analysis carries 76% automation pressure, while Verify source data accuracy carries 56% 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: $50,330 (May 2025, US national). Employment context: Data compilation role absorbed by analytics software. Typical education: High school diploma plus spreadsheet proficiency.

Wage vulnerability is 56, while transition feasibility is 64. 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.

• Very high substitution pressure
• Analytics tools absorb compilation
• Data verification offers the bridge

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 Quality Analyst, such as own source-data validation.
3. By 90 days, compare internal openings and external postings for Data Quality Analyst or Research Data Coordinator and update your resume around measurable workflow outcomes.