Job Market Analysis

Summary

Job descriptions are long, inconsistent, and written to be read once. Reading a hundred of them to work out what the market actually wants is a job nobody finishes by hand.

This is a small Python tool that does it. It reads postings for Data and AI roles, pulls out the requirements, the nice-to-haves and the seniority level, and puts the result in a dashboard.

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How it works

Postings go in as JSONL. Each one is passed to Gemma 3 through Ollama, which returns the core requirements, the optional extras, and a read on seniority. Everything lands in an interactive Streamlit dashboard.

The pipeline is organised with domain-driven design. At this size that mostly means keeping the parsing, the model calls and the reporting separable, so changing one does not quietly break the others.

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Tech

Python 3.12 or newer, Ollama with Gemma 3, Streamlit, and uv for dependencies.

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What I would change

Running the model locally was a deliberate trade. It costs nothing per posting and the data never leaves my machine. In exchange it is slower than an API call and occasionally misreads a requirement. For a tool I point at a few hundred postings, that was the right side of the trade. If it ran daily, it would not be.

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Source

The code is public: https://github.com/rasyidev/job-market-analysis