Headline risk
34%
High RiskData Analyst
This page reuses the same role shell as Singapore, but the component occupations are mapped onto the United States layer so the score, context, and support bundle reflect US public evidence.
Why this score
87% of tasks overlap with current AI
45% human advantage from judgment & presence
67% demand buffer from the local labour market
These factors combine multiplicatively — larger bars do not mean proportionally larger contributions to the final score.
Weighted overlap across component occupations
Human coordination and physical presence protection
Blended local-market buffer for this role
Component coverage and mapping quality
Workflow profile
Heuristic workflow context blended from the role mix. This explains the score; it is not used as a direct local-market forecast.
Workflow dimensions (0 = low, 1 = high)
United States support
Evidence bundle
Weighted task overlap from O*NET statements and Anthropic penetration
Median annual wage from BLS OEWS
Employment projections and openings from BLS
Preparation and entry requirements from O*NET and BLS
Support snapshot
Job zone
4The occupation usually needs substantial preparation and experience.
Median wage
USD 125,770USD 90,970 to USD 164,860
Openings
2.4K21.8% projected change
Median age
n/aNo CPS age profile published.
Occupation profile
Analyze statistical data, such as mortality, accident, sickness, disability, and retirement rates and construct probability tables to forecast risk and liability for payment of future benefits. May ascertain insurance rates required and cash reserves necessary to ensure payment of future benefits.
Job Zone 4 · Moderate preparation
The occupation usually needs substantial preparation and experience.
Task primitives
Matched task weight share: 100% · Effective coverage: 5%
Concentration: 100%
Wage context
Median annual
USD 125,770
Mean annual
USD 134,990
Hourly median: USD 60
Employment: 28,340 workers
10th percentile: USD 75,240
90th percentile: USD 206,430
Demand outlook
2024 employment
33.6K
2034 employment
40.9K
Openings: 2.4K
Projected change: 21.8%
Education: Bachelor's degree
Work experience: None
On-the-job training: Long-term on-the-job training
Median wage in projections: USD 125,770
Employment of actuaries is projected to grow 22 percent from 2024 to 2034, much faster than the average for all occupations.
Requirements and friction
Telework: 62.8% · Telework: 37.2% · Credentials: 7.6%
Narrative and skills
Actuaries use mathematics, statistics, and financial theory to analyze the economic costs of risk and uncertainty.
Most actuaries work for insurance companies. Although most work full time in an office setting, some actuaries who work as consultants travel to meet with clients.
Actuaries typically need a bachelor’s degree to enter the occupation and must pass a series of exams to become certified. They must have a strong background in mathematics, statistics, and business.
The median annual wage for actuaries was $125,770 in May 2024.
Employment of actuaries is projected to grow 22 percent from 2024 to 2034, much faster than the average for all occupations.
Jobs: 33,600
Median pay: USD 125,770
Employment outlook: Employment of actuaries is projected to grow 22 percent from 2024 to 2034, much faster than the average for all occupations.
Openings: 7,300
Tasks and tools
- 1. Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits. · AI use 0%
- 2. Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business. · AI use 0%
- 3. Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates. · AI use 0%
- 4. Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public. · AI use 0%
- 5. Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums. · AI use 0%
- 6. Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information. · AI use 0%
Work context
- E-Mail: 5.0/5
- Indoors, Environmentally Controlled: 5.0/5
- Spend Time Sitting: 4.9/5
- Face-to-Face Discussions with Individuals and Within Teams: 4.6/5
- Telephone Conversations: 4.5/5
- Importance of Being Exact or Accurate: 4.4/5
Tech density
6/6
6 hot · 6 in demand
Work pace
4.7/5
Average of the strongest work-context signals.
Worker profile
No CPS age profile published.
Support note
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.
Source vintage
O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Component occupations
Actuaries
15-2011 · 40% weight
Operations research analyst
Support bundle: Moderate preparation
Public relations specialists
27-3031 · 30% weight
Market research analyst
Support bundle: Moderate preparation
Mathematical science teachers, postsecondary
25-1022 · 30% weight
ICT business process consultant/Business analyst
Support bundle: Extensive preparation
Methodology
Shared spine
structural_pressure = exposure × (1 - bottleneck)
Country layer
headline_risk = structural_pressure × (1 - country_demand_resilience)
Published limitations
This is a synthetic role view built from mapped occupations. It reuses the same shell and visual components as the Singapore role pages, but only the US sources that actually exist are rendered here.