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JakobFerschin/README.md

Jakob Ferschin

Software Engineer building at the intersection of data systems and business logic. Currently studying Business Informatics at WU Wien and joining the Oesterreichische Entwicklungsbank (OeEB) as a Mid-Office IT Intern this summer.


💻 Engineering

An enterprise-grade B2B platform for public procurement analysis and predictive winner matching in the EU/DACH region.

  • Features: Deterministic "Non-LLM First" data pipeline, CPV-based historical matching algorithm, and an executive intelligence dashboard.
  • Stack: Next.js, TypeScript, Supabase, Tailwind CSS

An LLM-powered platform that automates pre-merger due diligence memos for strategy consulting.

  • Features: Structured JSON outputs via Pydantic, dynamic source-provenance tagging, and automated DOCX memo generation.
  • Stack: Python, Streamlit, Supabase, OpenAI/GLM APIs

A full-stack molecular property prediction system for drug discovery.

  • Features: DMPNN inference predicting 12 toxicity endpoints directly from SMILES inputs in under 500ms per batch.
  • Stack: PyTorch Geometric, FastAPI, React, TypeScript, RDKit

🏢 Experience

  • IT Associate @ BCS Business Consulting Society Vienna
  • Director of DACH Expansion @ Handprint Tech

🛠️ Stack

TypeScript Python Next.js React SQL Supabase FastAPI PyTorch n8n Tailwind CSS


📫 Connect

LinkedIn · jakob_ferschin@protonmail.com

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    AI-powered M&A Due Diligence Memo Generator with mandatory source citations, PDF extraction, and DOCX export. (Architectural showcase for Pydantic/Streamlit/LLM integration).

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    Full-stack molecular property prediction system using Directed Message-Passing Neural Networks (DMPNN).

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  4. GovTender GovTender Public

    An enterprise-grade B2B platform for deterministic public procurement analysis and predictive winner matching in the EU/DACH region. Built with a non-LLM first architecture, Next.js, and Supabase.

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