Predictive Machine Learning & Risk Analytics
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Cost-sensitive fraud triage systems, calibrated clinical risk models, and multivariate sensor telemetry. • 7 items • Updated
title string | id string | subtitle string | description string | licenses list | keywords list | resources list |
|---|---|---|---|---|---|---|
Global AI & Machine Learning Salaries (2023-2026) | jayangupta00/global-ai-machine-learning-salaries | 5,000+ Verified AI, ML, & Data Science Compensation Records Across 10 Countries | # 💼 Global AI & Machine Learning Salaries Dataset (2023–2026)
## 📌 Overview
This dataset captures compensation dynamics, remote-work distribution, and technical skill requirements across **5,000+ AI and Data Science professionals** worldwide between 2023 and 2026. The explosive adoption of Generative AI, Large Langu... | [
{
"name": "CC0-1.0"
}
] | [
"artificial intelligence",
"salaries",
"machine learning",
"deep learning",
"exploratory data analysis"
] | [
{
"path": "ai_job_market_salaries.csv",
"description": "Tabular records of AI, ML and Data Science compensation across global markets.",
"schema": {
"fields": [
{
"name": "work_year",
"type": "integer",
"description": "Year of salary observation (2023-2026)."
... |
Global AI & Machine Learning Salaries Telemetry 📊
A curated compensation benchmark dataset detailing global compensation telemetry across artificial intelligence, machine learning, data engineering, and data science roles.
- Associated Space: Global AI & ML Salary Estimator
- Author: Jayan Gupta
📋 Schema Overview
| Column | Type | Description |
|---|---|---|
work_year |
int | Year the compensation was recorded (2023-2026). |
experience_level |
string | EN (Entry), MI (Mid), SE (Senior), EX (Executive). |
employment_type |
string | FT (Full-time), PT (Part-time), CT (Contract), FL (Freelance). |
job_title |
string | Standardized role title (e.g. MLOps Engineer, Data Scientist). |
salary_in_usd |
float | Total annualized base salary normalized to USD. |
employee_residence |
string | ISO country code of employee residence. |
remote_ratio |
int | 0 (On-site), 50 (Hybrid), 100 (Fully Remote). |
company_location |
string | ISO country code of hiring entity. |
company_size |
string | S (<50 employees), M (50-250), L (>250). |
💻 Python Quickstart
from datasets import load_dataset
import pandas as pd
# Load dataset
dataset = load_dataset("Jayan12321/global-ai-machine-learning-salaries-2026")
df = pd.DataFrame(dataset["train"])
# Compute median salary by role seniority
median_comp = df.groupby("experience_level")["salary_in_usd"].median()
print(median_comp)
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