AMS Job Market Analysis Tool

Overview#

Python tool to collect and analyze publicly available remote job listings from the Austrian Public Employment Service (AMS).

Exports:

Job titles
Descriptions
Excel datasets

Second script:

Analyzes programming language demand
Generates trend graphs

Technical Highlights#

Automated collection of publicly available job listings
Structured extraction of titles and descriptions
Data cleaning and normalization pipeline
Programming-language demand analysis
Excel export for stakeholder-friendly reporting
Chart generation for trend visibility

Key Lessons#

Small automation workflows can produce decision-ready market insights
Data normalization is essential before trend analysis
Export format matters when sharing results with non-technical stakeholders
Public datasets often require structured preprocessing before becoming useful

Technologies#

Python
HTTP data extraction
Pandas
Excel export
Data visualization

Flowchart

flowchart TD
    A[AMS remote listings] --> B[Python collector]
    B --> C[Raw dataset]
    C --> D[Normalize and deduplicate]
    D --> E[Extract language keywords]
    E --> F[Aggregate demand frequency]
    F --> G[Excel export]
    F --> H[Demand trend charts]

Details

The AMS toolchain was built as a repeatable data collection and analysis workflow for remote software roles. Script one retrieves listings, parses structured fields, and stores normalized records for downstream processing. Data quality controls address duplicated posts and inconsistent text fragments. Script two analyzes language and technology demand by scanning job descriptions and aggregating frequency trends. The output includes both tabular datasets and visual summaries. Excel export was selected for portability, enabling distribution to non-technical stakeholders without additional tooling. The project demonstrates how lightweight automation can transform fragmented public listings into structured, decision-ready market insights.

Gallery

3 images