datamatastudios/ai-requirements-index

Datamata AI Requirements Index

How fast AI skills are becoming a job requirement: the share of active tech job listings mentioning AI skills, tracked over time and split by skill tier (generative AI vs classic ML), job category and seniority. Listing-level shares — a listing counts once per tier no matter how many AI skills it lists.

• Latest snapshot: 2026-08-05
• Rows in this release: 2529
• Updated: daily
• Licence: CC BY 4.0 — free to use and adapt, including commercially, with attribution.
• Source & methodology: https://www.datamatastudios.com/datasets/ai-requirements-index

Quickstart

import pandas as pd

# Stream straight from the Hub — no download step needed
df = pd.read_csv("hf://datasets/datamatastudios/ai-requirements-index/ai-requirements-index.csv")

# Headline: share of all data jobs mentioning any AI skill, over time
headline = df[(df.category == "data") & (df.seniority == "all") & (df.tier == "any_ai")]
print(headline[["snapshot_date", "pct"]].tail(10))

Or load it with the 🤗 datasets library:

from datasets import load_dataset

ds = load_dataset("datamatastudios/ai-requirements-index")

What you can answer with it

• What share of tech job listings now mention AI skills and how fast that share is rising.
• Whether generative-AI skills (tier = genai) are growing faster than classic ML (tier = ml).
• Whether entry-level roles are expected to know AI, via the seniority split.
• Which job categories (data, engineering, product, devops, security) are adopting AI requirements fastest.
• How often AI skills are hard requirements vs nice-to-haves (required_count vs listings_with_ai).

Columns

Column	Type	Description
snapshot_date	string	UTC date the snapshot was taken (YYYY-MM-DD).
category	string	Job category: data, engineering, product, devops, security or ai.
seniority	string	Seniority split: all, entry, mid, senior, lead or unknown.
tier	string	AI skill tier: genai (LLM-era skills like RAG and fine-tuning), ml (classic ML like PyTorch and scikit-learn) or any_ai (either).
listings_with_ai	number	Active listings mentioning at least one skill in the tier.
total_listings	number	All active listings in the category/seniority group on the snapshot date.
pct	number	listings_with_ai as a percentage of total_listings.
required_count	number	Listings where a tier skill is a hard requirement rather than nice-to-have. Blank for legacy rows.

How it is built

Each day we snapshot every active job listing scraped from public company career pages and job boards, extract skills from the listing text with a curated taxonomy and record the share of listings mentioning at least one skill per AI tier. Shares are listing-level, so a listing that names five AI skills counts once. Full method and known limitations: https://www.datamatastudios.com/methodology.

Citation

Datamata Studios. "Datamata AI Requirements Index." 2026-08-05. https://www.datamatastudios.com/datasets/ai-requirements-index. Licensed under CC BY 4.0.