Data Scientist with strong scientific curiosity, building AI Agents using RAG, MCP, n8n, and end-to-end automation frameworks.
I enjoy combining machine learning with scientific reasoning to solve real-world problems in Finanace, Automobile, biotech, automation, and AI systems.

• PhD — Trinity College Dublin

• Master’s — University of Hyderabad

I have implemented and fine-tuned a variety of ML and DL models in real production use cases.

Machine Learning Models:
Linear Regression, Logistic Regression, SVM, Decision Trees (DT), Random Forests (RF), KNN, Naive Bayes (NB),
Gradient Boosted Decision Trees (GBDT), XGBoost

Deep Learning Models:
Deep Neural Networks (DNN), Convolutional Neural Networks (CNN),
Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM)

AI & Agentic Systems

• Retrieval-Augmented Generation (RAG)
• Model Context Protocol (MCP)
• n8n automation
• Local LLMs and OpenAI models
• Vector Databases (ChromaDB, Pinecone)
• LangChain and LangGraph

Machine Learning & Data

• Python, pandas, NumPy
• scikit-learn, XGBoost
• TensorFlow / PyTorch (learning)
• Data cleaning, modeling, and evaluation

Developer Tools

• Git & GitHub
• Docker, WSL
• Jupyter Notebook, Google Colab

• Building AI Agents using RAG + MCP for scientific and operational automation
• Stability testing & cold-chain automation concepts
• ReagentXchange (surplus reagent marketplace – idea stage)
• ML experiments with time-series and options trading data

• RAG-ICH-Q6B
• QC-Stability-Design-Helper
• ReagentXchange-MVP
• Simple-Options-Signals

• LinkedIn: www.linkedin.com/in/sivadataphd
• Email: [email protected]