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Core Data Science - strong foundation in statistics, probability, ML/DL algorithms, and model evaluation techniques Python & SQL - solid proficiency; you think in Python and speak fluent SQL ML Toolkit - hands-on experience with Pandas, NumPy, Scikit-learn, and at least one deep learning framework (PyTorch / TensorFlow) Generative AI & LLMs - working knowledge of large language models (GPT, LLaMA, etc.), prompt engineering, and prompt optimization techniques RAG & Embeddings - understanding of Retrieval-Augmented Generation architectures, vector databases (FAISS, Pinecone, Weaviate), and embedding strategies LLM Frameworks - exposure to LangChain, LangGraph, or similar orchestration frameworks Communication - ability to distill complex analysis into clear insights for both technical and non-technical audiences Mindset - strong problem-solving instincts, intellectual curiosity, and a bias for action.