Direction and structure of demand in the AI & Data job market
A grounded look at open jobs across 508 S&P 500 corporations, tech leaders, and the European market. We collect data straight from corporate career portals into a local DuckDB warehouse and publish it as open static telemetry.
Market Vitality & AI/Data Hiring Intensity
Share of AI and data positions relative to the total corporate hiring pool
S&P 500 AI/Data Intensity
4,646 AI/Data out of 413,801 open roles
Total Active AI/Data Jobs
Total hiring pool: 461,606 open positions
S&P 500 Adoption Breadth
94% of S&P 500 corporations actively hiring data talent
Geographic Scope (US/CA vs. Europe)
Czech Republic (CZ): 176 active positions
Top 10 Corporations by Open AI & Data Positions
Absolute count of active data and AI job postings in the current snapshot
| Metric / Technology | Open AI/Data Positions |
|---|---|
| Google (Alphabet) | 259 |
| UnitedHealth Group | 202 |
| Databricks | 182 |
| OpenAI | 177 |
| Anthropic | 128 |
| TE Connectivity | 121 |
| Amazon | 73 |
| Amgen | 69 |
| Arthur J. Gallagher & Co. | 57 |
| Citigroup | 56 |
Geographic Market Distribution
TOP 8 MARKETSLongitudinal collection is in its initial baseline phase (Q4 2026). Early snapshot growth reflects progressive onboarding of S&P 500 ATS sources; full MoM/WoW market dynamics activate after 3 completed monthly cohorts.
Most In-Demand AI & Data Roles
Highest hiring volume across S&P 500 firms with detailed skill requirements and profiles
AI Developer
The AI Developer is the fastest-growing engineering profile across S&P 500 enterprises, positioned directly at the crossroads of software engineering and applied AI. Rather than training models from scratch, these engineers integrate Large Language Models (LLMs), build agentic workflows, and deploy production-grade RAG pipelines into products.
Data Engineer
The Data Engineer builds and maintains the data foundations required by modern analytics and AI systems. Without dependable data ingestion, clean transformation pipelines, and rigorous observability, reporting and machine learning projects stall. This keeps Data Engineering among the most consistently recruited technical tracks.
Data Analyst
The Data Analyst remains central to helping organizations make decisions grounded in solid evidence. In modern data stacks, the focus has moved from ad-hoc spreadsheets to deep business domain knowledge, clean metric definition, and working with governed semantic layers.
Machine Learning Engineer
The Machine Learning Engineer takes experimental models out of notebooks and puts them into reliable, production-ready services. Job postings emphasize end-to-end ownership of the ML lifecycle: building feature pipelines, distributed training, fine-tuning, and low-latency inference at scale.
Data Scientist
The Data Scientist role has matured significantly: organizations now prioritize specialists in causal inference, rigorous experimentation (A/B testing), optimization algorithms, and predictive modeling tied to revenue and risk reduction over generic data exploration.
Research Scientist (AI/ML)
The Research Scientist in AI/ML focuses on advancing core algorithmic frontiers: designing novel neural architectures, multimodal systems, reinforcement learning methods, and post-training alignment techniques.
Technology & Cloud Stack
Adoption of hyperscalers (AWS, GCP, Azure), modern data platforms, and core libraries
Cloud Hyperscalers & Modern Data Platforms
Active job postings explicitly requiring the given cloud or data platform
| Metric / Technology | Job Postings |
|---|---|
| AWS | 758 |
| Microsoft Azure | 666 |
| Google Cloud Platform | 638 |
| Databricks | 541 |
| Snowflake | 315 |
| BigQuery | 112 |
| Amazon Redshift | 55 |
Most Demanded Technologies & Competencies (% Penetration)
Share of all active AI and data job postings
| Metric / Technology | Job Posting Penetration |
|---|---|
| Python | 36.32% |
| SQL | 22.44% |
| AI Agents / Agentic AI | 18.77% |
| LLM / Large Language Models | 18.64% |
| GenAI / Generative AI | 14.36% |
| AWS | 12.58% |
| Data Governance & Quality | 11.15% |
| Microsoft Azure | 11.05% |
| Apache Spark | 10.69% |
| Google Cloud Platform | 10.59% |
| MLOps / Evals & Observability | 10.36% |
| R | 9% |
Most Frequent Technology Pairings (Skill Co-occurrence)
Tool combinations requested together within the same job posting (ranked by volume and Jaccard similarity)
Industry Heatmap: S&P 500 Sectors & Seniority Distribution
Ordered by AI/Data Hiring Intensity (% of data roles out of all open positions in the sector)
| GICS Sector (S&P 500) | Firms | AI/Data Jobs | AI Intensity | GenAI Share | Junior/Entry | Mid | Senior | Staff/Principal | Lead/Manager | Executive |
|---|---|---|---|---|---|---|---|---|---|---|
| Communication Services | 25 | 903 | 4.64% | 25% | 35 | 379 | 180 | 208 | 100 | 1 |
| Utilities | 31 | 101 | 3.24% | 1% | 4 | 43 | 29 | 13 | 12 | 0 |
| Energy | 21 | 108 | 3.16% | 7.4% | 5 | 80 | 5 | 5 | 12 | 1 |
| Financials | 76 | 727 | 2.09% | 26.3% | 43 | 221 | 180 | 90 | 96 | 97 |
| Materials | 24 | 133 | 1.92% | 37.6% | 5 | 57 | 43 | 9 | 19 | 0 |
| Information Technology | 76 | 947 | 1.86% | 35.7% | 67 | 328 | 237 | 212 | 87 | 16 |
| Real Estate | 30 | 39 | 1.6% | 2.6% | 1 | 18 | 14 | 2 | 3 | 1 |
| Health Care | 60 | 651 | 1.15% | 20.9% | 17 | 152 | 183 | 91 | 151 | 57 |
| Industrials | 83 | 459 | 0.93% | 20.9% | 19 | 198 | 123 | 60 | 57 | 2 |
| Consumer Discretionary | 48 | 429 | 0.36% | 20% | 13 | 143 | 120 | 47 | 91 | 15 |
| Consumer Staples | 34 | 149 | 0.22% | 16.1% | 4 | 39 | 40 | 29 | 30 | 7 |
Hierarchy Shift: Hands-on (IC) vs. Management
Ratio of executive and management positions relative to individual contributors
14.1 : 1 in favor of Hands-on IC
The market overwhelmingly demands direct technical execution. Fully 93.36% of all open roles are Individual Contributors (engineers, analysts, scientists), while pure management accounts for 6.64%.
IC Share by Domain
Workforce intelligence integration tracking 187,585 occupied Data & AI seats across 627 enterprises. Measures Hiring Intensity Ratio: open job postings relative to currently occupied team seats.
Demand Distribution by Seniority
Mid, Senior, and Staff/Principal engineering roles dominate; entry-level Junior positions represent only ~4.4%
| Metric / Technology | Active Job Postings |
|---|---|
| Junior / Entry | 260 |
| Mid | 2137 |
| Senior | 1573 |
| Staff / Principal | 1082 |
| Lead / Manager | 769 |
| Executive | 204 |
Installed Base vs. Hiring Velocity (Hiring Intensity Ratio)
Comparing current employed data/AI headcount against active open job postings across enterprise teams
Installed Team Base vs. Open Positions by Role
Log-scale contrast: massive legacy roles (Data Analyst, Engineer) vs. aggressive expansion in AI Developers
| Metric / Technology | Occupied Team Seats (Headcount) | Active Open Postings |
|---|---|---|
| Data Analyst | 51483 | 977 |
| Data Engineer | 46799 | 988 |
| Data Scientist | 29182 | 863 |
| Research Scientist | 17181 | 380 |
| Machine Learning Engineer | 9867 | 912 |
| BI Developer | 7790 | 77 |
| BI / Analytics Manager | 5228 | 101 |
| Data Architect | 3610 | 171 |
Top Expanding Enterprise Teams (Hiring Intensity Ratio)
EXPANSION %Companies aggressively expanding their Data & AI teams relative to current size (min. 20 team members)
Trend Radar: Adoption of Emerging Paradigms
Frequency of high-signal terms (GenAI, Agentic AI, RAG, Evals, Semantic Layer, Vertex AI, Dataform) in job descriptions
Emerging Technology Penetration Radar (%)
Percentage of all AI/Data postings mentioning the concept
| Metric / Technology | Posting Penetration (%) |
|---|---|
| AI Agents | 18.77% |
| LLM | 18.64% |
| GenAI | 14.36% |
| MLOps | 10.36% |
| RAG (Retrieval-Augmented Generation) | 8.22% |
| Fine-Tuning | 5.38% |
| Vector Databases | 3.27% |
| Vertex AI | 1.21% |
Detailed Job Posting Signal Matrix
N = 6,025 POSTINGSAI Agents / Agentic AI
Category: AI/ML • S&P 500: 784×
LLM / Large Language Models
Category: AI/ML • S&P 500: 744×
GenAI / Generative AI
Category: AI/ML • S&P 500: 702×
MLOps / Evals & Observability
Category: MLOps • S&P 500: 385×
RAG (Retrieval-Augmented Generation)
Category: AI/ML • S&P 500: 383×
Fine-Tuning
Category: AI/ML • S&P 500: 200×
Vector Databases
Category: Database • S&P 500: 162×
Vertex AI
Category: Cloud • S&P 500: 66×
Semantic Layer
Category: Business • S&P 500: 26×
Google Cloud Dataflow
Category: Framework • S&P 500: 23×
Dataform
Category: Framework • S&P 500: 2×