JobsLens AI

Interactive Dashboard for Digital/AI Job Demand & Supply Analysis

Overview

JobsLens AI analyzes the global landscape of digital and AI job markets, mapping supply and demand trends across countries, industries, and skill types. Our interactive dashboard helps policymakers and stakeholders identify where digital/AI job opportunities are rising or lagging, enabling data-driven decisions to maximize inclusion and productivity in the rapidly evolving digital economy.

Team Members

  • Maryam Shahbaz Ali
  • Paul Suhwan Lee
  • Rafael Kovashikawa
  • Stevens Cadet
  • Yingquan Li

Challenge Category Area

Category V: Toward New Insights on Job Trends

Challenge 8: Demand & supply in digital/AI jobs - Build an interactive dashboard showing where digital/AI job demand and supply are rising or lagging across countries, industries, and skill types.

Project Description

JobsLens AI addresses the critical challenge of understanding digital and AI labor market dynamics in an era of rapid technological disruption. With over 1.2 billion youth entering the labor force by 2030 in developing countries, understanding where digital opportunities exist and where skills gaps persist is essential for inclusive economic growth.

Our solution provides:

  1. Real-time Demand & Supply Mapping: Interactive visualizations showing digital/AI job demand vs. available talent across countries and regions
  2. Skills Gap Analysis: Identification of mismatches between required skills and workforce capabilities
  3. Industry-specific Insights: Sector-by-sector breakdown of ICT manufacturing, ICT services, and AI-related employment trends
  4. Predictive Analytics: Trend analysis to help countries anticipate future digital opportunities and prepare their workforce accordingly

Key Features

  • Multi-dimensional filtering (country, region, industry, skill type, time period)
  • Supply-demand ratio calculations and visualizations
  • Geographic heat maps showing digital job concentration
  • Skills taxonomy mapping to identify training priorities
  • Comparative analysis tools for benchmarking across countries

Methodology

Data Integration

  • Consolidation of data from World Bank Global Labor Database (JOIN), and Stanford’s HAI (https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf)
  • Harmonization of country codes, industry classifications, and time periods
  • Creation of unified demand and supply metrics

Technical Implementation

  • Python-based data processing and analysis
  • Interactive dashboard built with modern web technologies
  • Responsive visualizations for accessibility across devices

Expected Impact

  1. Immediate Impact: Provide policymakers with actionable insights on current digital skills gaps
  2. Strategic Planning: Enable countries to design targeted education and training programs
  3. Investment Decisions: Guide private sector investment in workforce development
  4. Regional Collaboration: Identify opportunities for cross-border talent mobility and knowledge sharing

Data Sources

  • World Bank Global Labor Database
  • HAI Database

Project Structure

JobsLens_AI/
β”œβ”€β”€ data/                  # Data files (raw, processed, external)
β”œβ”€β”€ notebooks/             # Jupyter notebooks for analysis
β”œβ”€β”€ src/                   # Python source code
β”œβ”€β”€ dashboard/             # Dashboard application files
β”œβ”€β”€ visualizations/        # Generated charts and graphics
β”œβ”€β”€ docs/                  # Documentation and reports
└── project.md            # This file