I'm making a deliberate shift into data. Driven by a want to work close to real problems, understand how things actually work, and build infrastructure that helps teams make better decisions. Equipped with a B.A. in Statistics & Psychology (BGU, GPA 90) and the experience of running a small business, I bring a high degree of responsibility, adaptability, and ownership. I care about the full stack - from a clean SQL cohort to the Telegram alert firing and UI. Currently i'm building data pipelines and AI-native workflows: an autonomous job scanner in production, a multi-agent analytics platform, and a volunteer data pipeline for Greenpeace Israel.
Volunteer data pipeline for Greenpeace Israel's campaign team. Built a full ETL pipeline pulling Google and Meta Ads data via Coupler.io → Python transformation scheduled on GitHub Actions → Google Sheets via authenticated API → Looker Studio dashboard. Enables cross-platform campaign performance and costs analysis for better marketing decisions.
Multi-agent data analysis platform that covers DIG framework - a paradigm of data analysis using LLMs. Upload any Data file → a hardcoded pandas layer extracts facts → 5 specialized LLM agents interpret and analyze along with the user → Consolidate Living Report with real insights built along the progress. Key decisions made: to control LLMs hallucinations pandas and plotly doing all the math (LLM never sees raw data), 'Pydantic v2' validates every agent output, parametric orchestration keeps Python in control of flow.
Autonomous AI intagrated job scanner running in production. Monitors Telegram job groups 3x daily, scores matches against my DA profile using GPT-4o mini, scoring sotrage in Supabase for deep analysis and improvmants. Telegram alerts sends for high-fit roles. Anti-hallucination design: temperature=0, Pydantic validation, confidence scoring tracking The pipline is complitly adjustable for diffarent canidatas with manual readme file in GitHub .
