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  • India
  • 09:00 (UTC -12:00)

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HelloShibani/README.md

👋 Hey! Nice to see you.

I'm [Shibani Roychoudhury] 😄

👩‍💻 Data Scientist specializing in NLP, Generative AI, and decision-support systems. 🧠 Passionate about building explainable, modular AI pipelines using LangChain, vector databases, and LLMs. 🚀 Former software engineer (15+ yrs) turned AI system designer—focusing on real-world ML applications in insurance, HR, and e-commerce. 📦 Projects include multi-agent RAG assistants, recommender systems with fallback logic, and Dockerized AI pipelines. 🔍 Always exploring the bridge between research and usable AI.

Currently looking for a internship / job 🔎 Email me

🔧 Tools: Python, SQL, LangChain, Hugging Face, ChromaDB, FastAPI
🧠 Focus: NLP, Generative AI, Recommender Systems, Explainable ML
🚀 What I build: Modular AI pipelines, document-grounded assistants, Dockerized ML systems

⚙️ Languages & Tools I Work With

📫 How to reach me:

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  1. multi-strategy-recommendation-pipeline multi-strategy-recommendation-pipeline Public

    A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world …

    Jupyter Notebook 2

  2. Sentiment-Based-Product-Recommendation-Analysis-Revision Sentiment-Based-Product-Recommendation-Analysis-Revision Public

    Revised sentiment-based product recommendation analysis with improved features & model tuning.

    Jupyter Notebook 1

  3. HelpMate_AI HelpMate_AI Public

    Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

    Jupyter Notebook

  4. Sentiment-Based-Product-Recommendation-Analysis Sentiment-Based-Product-Recommendation-Analysis Public

    Sentiment-based recommendation system leveraging NLP for personalized product suggestions.

    Jupyter Notebook

  5. Lead_Scoring_Case_Study Lead_Scoring_Case_Study Public

    Lead Scoring Case Study: Analyzing and prioritizing leads using data-driven techniques to enhance sales efficiency

    Jupyter Notebook