Employed persons with tertiary education in STEM fields by occupation (Eurostat)
Employed persons with tertiary education in STEM fields by occupation: data for 31 countries and regions. Source: Eurostat. Compare countries, chart the series, or query it free as CSV, JSON, or API at https://autario.com/data/employed-persons-with-tertiary-education-in-stem-fields-by-occupation-eurostat.
2,114 rows · 15 columns · 31 regions/countries · Last data pull: 2026-08-30
This dataset tracks employed persons with tertiary education in science, technology, engineering, and mathematics fields, broken down by occupation across the European Union and member states. Understanding the distribution of STEM-educated talent across different occupations reveals how advanced economies allocate their highest-skilled human capital and identifies sectoral demand for specialized expertise.
The dataset covers 28 European entities with 2,114 data records spanning from 2021 to 2024. All 28 entities, including Austria, Belgium, Bulgaria, Cyprus, and the Czech Republic, reported data through 2024. This comprehensive geographic and temporal scope enables both cross-country comparisons of STEM employment patterns and analysis of how occupational distribution shifted across the four-year period.
Researchers and labor economists use this data to understand workforce composition, identify skills gaps, and forecast demand for STEM talent by occupation type. Policymakers reference occupational breakdowns to guide education and immigration strategies, while business analysts track employment trends to anticipate hiring needs in specialized technical roles. The data reveals whether STEM graduates concentrate in engineering and computing roles or distribute across broader professional occupations.
This publicly available Eurostat resource supports evidence-based workforce planning, academic research on skills markets, and strategic business intelligence for companies competing for technical talent across Europe.
Query via API instead of re-downloading. Live REST API and instant in-browser charts stay source-synced with the primary publisher, no CSV exports to maintain, MCP-ready for Claude, ChatGPT, and Cursor. Use this dataset in Power BI or Excel via the public API, no key required.
Indicator: eu_employed_persons_with_tertiary_education_in_stem_fields_by_o_3d4926
Show API + MCP usage examples
# Get data (JSON)
curl "https://autario.com/api/v1/public/datasets/3d4926d1-16dd-484d-8995-eff18a45e2a1/data?limit=100"
# Filter and sort on this dataset's own columns (replace the bracketed placeholder with a value)
curl "https://autario.com/api/v1/public/datasets/3d4926d1-16dd-484d-8995-eff18a45e2a1/data?filter=geo:eq:<geo>"
# CSV export (login required)
curl "https://autario.com/api/v1/public/datasets/3d4926d1-16dd-484d-8995-eff18a45e2a1/export"
# Python
import requests
r = requests.get("https://autario.com/api/v1/public/datasets/3d4926d1-16dd-484d-8995-eff18a45e2a1/data", params={"limit": 100})
data = r.json()["data"]
Full API docs · Agent guide · MCP setup for Claude / ChatGPT / Cursor · No API key required for public data.
For AI agents
Statistics for this dataset are precomputed server-side. One call returns finished, verified numbers with provenance. No row download, no local computation:
# Finished stats, zero rows (n, min, max, avg, first/last)
curl "https://autario.com/api/v1/public/datasets/3d4926d1-16dd-484d-8995-eff18a45e2a1/data?summary_only=true"
# Summary statistics (mean/median/std/quartiles/skew)
curl "https://autario.com/api/v1/public/ontology/stats/describe?indicator=eu_employed_persons_with_tertiary_education_in_stem_fields_by_o_3d4926&entity=AT"
# Growth: latest period-over-period change
curl "https://autario.com/api/v1/public/ontology/stats/pct-change?indicator=eu_employed_persons_with_tertiary_education_in_stem_fields_by_o_3d4926&entity=AT"
# Smoothed trend: 5-period rolling mean
curl "https://autario.com/api/v1/public/ontology/stats/rolling?indicator=eu_employed_persons_with_tertiary_education_in_stem_fields_by_o_3d4926&entity=AT&window=5"
# Correlate with any other indicator (ids via /api/v1/public/ontology/indicators)
curl "https://autario.com/api/v1/public/ontology/stats/correlate?entity=AT&a=eu_employed_persons_with_tertiary_education_in_stem_fields_by_o_3d4926&b=<indicator_id>"
The same operations are MCP tools (describe, pct_change, rolling_stats, correlate) via npx autario-mcp or the remote endpoint https://autario.com/mcp. MCP responses default to TOON format and use about 74% fewer tokens than pretty JSON. Details: agent guide and llms.txt.
Original source: Eurostat
Sample rows
| geo | dataflow | last_update | freq | isco08 | time_period |
|---|
| AT | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2021 |
| AT | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2022 |
| AT | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2023 |
| AT | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2024 |
| AT | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2025 |
| BE | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2021 |
| BE | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2022 |
| BE | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2023 |
| BE | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2024 |
| BE | ESTAT:HRST_STEM_OCC(1.0) | 24/06/26 23:00:00 | A | OC1 | 2025 |
Related datasets
Topic hubs: Education · Employment & Labor
Keywords: EU, SDMX
Open Employed persons with tertiary education in STEM fields by occupation (Eurostat) on autario.com