I am an data professional. I work with data science, data engineering, data visualization, and LLM/AI.

3 minute read

Building JobsLens AI at World Bank DataDive 2025

Last week, I participated in the World Bank DataDive 2025, a 8-hour hackathon focused on extracting insights from global labor market data. Our team of five—Maryam Shahbaz Ali, Paul Suhwan Lee, Stevens Cadet, Yingquan Li, and myself—tackled Challenge 8: Digital/AI Job Demand & Supply Analysis.

TL;DR: We built JobsLens AI, an interactive Gradio dashboard that visualizes AI job market trends across countries, identifies skills gaps, and forecasts future demand.

The Challenge

The World Bank wanted to understand where digital and AI job opportunities are rising or lagging across countries, industries, and skill types. The goal was to help policymakers identify:

  • Countries with high AI job demand but low skilled workforce supply (skills gaps)
  • Emerging markets with growing AI sectors
  • Trends in AI talent concentration and investment

The Data

We worked with two primary datasets:

  1. Stanford HAI AI Index 2025 (528 observations, 232 columns)
    • Panel data spanning 2017-2024 for 66 countries
    • Key metrics: AI job postings, skills penetration, talent concentration, patents, investment
    • Target variable: ai_job_postings_perc_of_all_job_postings
  2. World Bank Global Labor Database (154 countries, cross-sectional)
    • Employment rates by sector and education level
    • Labor force participation, unemployment rates
    • Demographics and population statistics

Technical Approach

Data Integration Challenge

The fundamental problem was joining cross-sectional predictors (World Bank) with panel outcomes (HAI):

  • HAI: Multiple years per country (2017-2024)
  • World Bank: Single observation per country

We used a many-to-one merge, assuming labor market characteristics remain relatively stable over time.

Dashboard Development

Built with Gradio 4.0 and Plotly, the dashboard features 5 interactive tabs:

  1. 🌍 Global Overview: Choropleth map showing supply-demand gaps
  2. 📊 Country Comparison: Multi-country trend analysis (default: Brazil, Argentina, USA, Japan)
  3. 🏆 Rankings: Top countries by AI job demand, skills supply, investment, startups
  4. 🔮 Forecasts: 2024-2025 predictions using Ridge regression
  5. 🔬 Country Deep Dive: Detailed statistics for individual countries

Deployment

We deployed to Hugging Face Spaces for free hosting:

# Project structure
Team_Projects/JobsLens_AI/
├── app.py                    # Gradio dashboard
├── data/
│   ├── hai_full_database.csv
│   └── forecasts_2024_2025.csv
├── hf_space/                 # Git repo for HF Spaces
└── notebooks/
    └── EDA.ipynb

Tech stack:

  • Frontend: Gradio 4.0 with custom Plotly visualizations
  • Data: pandas, numpy
  • ML: scikit-learn (Ridge, Random Forest, Gradient Boosting)
  • Deployment: Hugging Face Spaces (free CPU tier)

Key Insights from the Data

Skills Gap Leaders (High Demand/Low Supply)

  • Switzerland: 1.41% AI job demand (highest forecasted)
  • Netherlands: 1.25%
  • United States: 1.36%

Supply-Demand Balance

The global AI job market shows:

  • Growing demand outpacing skills supply in developed economies
  • Emerging markets (Brazil, Mexico) showing moderate growth
  • Investment concentrated in US, China, EU
  • AI job postings grew ~3-5% annually in top markets
  • Skills penetration lagging by 2-3 years
  • COVID-19 accelerated remote AI hiring (2020-2021 spike)

What I Learned

Technical

  1. Small data ≠ deep learning: With only 83 observations, simpler models (Ridge) outperformed complex ones
  2. Feature engineering matters: Missing 14/24 features severely limited model performance
  3. Cross-sectional + panel merging: Requires careful assumptions about temporal stability
  4. Gradio is powerful: Built a full interactive dashboard in <2 hours

Hackathon Strategy

  1. Iterate fast: We went from EDA → model → dashboard → deployment in <8 hours
  2. Visualize early: Interactive plots helped us spot data issues quickly
  3. Deploy early: Having a live URL motivated us to polish the final product
  4. Team roles: Divided into data/modeling (me + Yingquan), visualization (Paul), storytelling (Maryam), infrastructure (Stevens)

Results & Recognition

While our forecasting model didn’t achieve production-ready accuracy, the exploratory dashboard successfully visualizes historical trends and provides policymakers with actionable insights on global AI skills gaps.

Future Improvements

If I were to continue this project:

  1. Use “All” subsample instead of “Urban” to get 24/24 features
  2. Time-series models: ARIMA or exponential smoothing might outperform ML on small data
  3. External data: Add OECD education stats, GitHub commit data by country
  4. Sector analysis: Break down AI jobs by industry (healthcare, finance, etc.)
  5. Interactive filtering: Let users select custom country groups and metrics

Try It Yourself

🔗 Live Dashboard: https://huggingface.co/spaces/rkovashikawa/jobslens-ai

Explore AI job trends for 66 countries, compare Brazil vs. USA, or dive into Switzerland’s AI ecosystem. The dashboard is free and requires no login.

Hackathon: World Bank DataDive 2025 Team: JobsLens AI Duration: 8 hours Tech: Python, Gradio, Plotly, scikit-learn, Hugging Face Spaces

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