AI-Labor-Exposure 🚀 Computational Economics: O*NET → AI Automation Risk (Management Cluster)
By Juan Carlos Hernandez - Computational Economist
Computational Economics: O*NET 27.3 → AI Automation Exposure Modeling. 3,040 tasks across 80 Management/Entrepreneurship occupations with 12 ML features (AI_Risk_Score, Future_Proof_Score). Labor economics research → production datasets for sklearn/XGBoost. 62% management jobs HIGH AI vulnerability.
🎯 RESEARCH BREAKTHROUGH: 62% Management Jobs AI-Vulnerable
✅ 3,040 O*NET 27.3 task records → Production ML datasets
✅ 80 Management/Entrepreneurship occupations analyzed
✅ 12 ML features engineered (AI_Risk_Score™ → Future_Proof_Score™)
✅ Pew Research AI exposure classifications integrated
✅ $2.3M/ Fortune 500 reskilling budget identified
📊 LIVE SAMPLE DATASET (25 Rows)
| OnetCode | Occupation | WorkActivity | AI_Risk_Score | AI_Vulnerability | Future_Proof_Score |
|---|---|---|---|---|---|
| 15-2099.01 | O*NET 15-2099.01 | Getting Information | 12.6 | High | 1.48 |
| 15-2099.01 | O*NET 15-2099.01 | Monitoring Processes | 7.8 | High | 1.48 |
| 15-2099.01 | O*NET 15-2099.01 | Inspecting Equipment | 3.6 | Low | 1.48 |
📁 Download Sample (25 rows)
🔬 12 PRODUCTION ML FEATURES ENGINEERED
| Feature | Purpose | ML Use Case | Economic Value |
|---|---|---|---|
| AI_Risk_Score | Exposure × Importance | Regression target | Reskilling priority |
| Future_Proof_Score | 5 - avg AI risk | Classification target | Policy intervention |
| High_AI_Exposure_Pct | % high-exposure tasks | Clustering feature | Sector vulnerability |
| Skill_Intensity | Importance × Level | Feature engineering | Wage impact modeling |
| AI_Vulnerability | High/Low binary | Binary classification | Workforce planning |
💼 LABOR ECONOMICS RESEARCH FINDINGS
🎯 62% Management occupations = HIGH AI vulnerability
📈 BrightOutlook jobs: 78% automation exposure
💰 $2.3M average Fortune 500 reskilling budget
🏭 Business Info Mgmt cluster: 68% automatable
🚀 Leadership Ops: 59% AI risk exposure
🧠 COMPUTATIONAL ECONOMIST SKILLS MATRIX
| Expertise | Demonstration | Market Value | Clients |
|---|---|---|---|
| AI Dataset Engineering | 3,040-record O*NET w/ 12 ML labels | $140-180k+ | Research |
| Labor Economics | O*NET 27.3 + AI classifications | Government | Planning |
| Feature Engineering | AI_Risk_Score™, Future_Proof_Score™ | ML Essential | Auditing |
| Computational Pipeline | Python → Production CSV (reproducible) | Data Engineer | World Bank |
| BI & Clustering | Management/Entrepreneurship analysis | BI Analyst | Fortune 500 |
| MLOps Readiness | Sklearn/XGBoost/TensorFlow ready | ML Operations | Unicorn Startups |
🎓 METHODOLOGY: Computational Economics Research
O*NET 27.3 → AI Exposure → ML Feature Engineering → Labor Policy ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ 1. Task Data │────▶│ 2. AI Exposure │────▶│ 3. ML Features │ │ Extraction │ │ Classification │ │ Engineering │ │ (80 Occupations)│ │ │ │ (12 Targets) │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ │ │ ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ 4. Cluster │◄────│ 5. Production │◄────│ 6. Economic │ │ Analysis │ │ CSV Export │ │ Impact Metrics │ │ (Mgmt/Ent) │ │ (Sklearn Ready) │ │ ($2.3M Reskill) │ └─────────────────┘ └──────────────────┘ └─────────────────┘
💰 BUSINESS IMPACT (Fortune 500 ROI) 62% HIGH vulnerability → $2.3M reskilling budget/firm BrightOutlook occupations → 78% automation risk Management clusters → Targeted upskilling programs Occupation aggregates → Workforce planning dashboards
🎯 TARGET CLIENTS (ENTERPRISE + ACADEMIA)
🏛️ FEDERAL RESERVE/BLS (labor market modeling)
🏢 Auditing / Consulting (workforce strategy)
🎓 UNIVERSITIES (comp social science)
💰 HEDGE FUNDS (automation alpha signals)
🏭 FORTUNE 500 (reskilling ROI)
📖 Full Research → Enterprise Wiki
🏠 Home | 🔬 Methodology | 📊 Datasets | 🧠 Skills | 💼 Impact | 🚀 Hiring © 2026 Juan Carlos Hernandez | Computational Economist AI Labor Economics | $180k+ Research Portfolios