Data Science Intern

You are applying for a Data Scientist Intern position with a 3-month project-based internship at our company. Your main responsibility will be to work closely with the data science and AI teams to develop, test, and optimize intelligent data-driven solutions. As a Data Scientist Intern, you will need to have strong Python programming skills, hands-on experience in prompt engineering, and a good understanding of Retrieval-Augmented Generation (RAG) workflows. Key Responsibilities: - Develop and maintain Python-based data pipelines and AI workflows. - Work on prompt engineering to fine-tune LLM responses for various business use cases. - Implement and test RAG pipelines integrating LLMs with external data sources. - Perform exploratory data analysis (EDA), preprocessing, and feature extraction. - Collaborate with cross-functional teams to design experiments and validate model outputs. - Document processes, findings, and code for reproducibility and scalability. Required Skills & Qualifications: - Strong proficiency in Python and libraries like Pandas, NumPy, and LangChain / LlamaIndex. - Understanding of Prompt Engineering techniques for LLM optimization. - Hands-on knowledge of RAG (Retrieval-Augmented Generation) implementation. - Familiarity with APIs, vector databases (e.g., FAISS, Pinecone, Chroma), and embeddings. - Good analytical, problem-solving, and communication skills. - Ability to work independently and deliver within timelines. You are applying for a Data Scientist Intern position with a 3-month project-based internship at our company. Your main responsibility will be to work closely with the data science and AI teams to develop, test, and optimize intelligent data-driven solutions. As a Data Scientist Intern, you will need to have strong Python programming skills, hands-on experience in prompt engineering, and a good understanding of Retrieval-Augmented Generation (RAG) workflows. Key Responsibilities: - Develop and maintain Python-based data pipelines and AI workflows. - Work on prompt engineering to fine-tune LLM responses for various business use cases. - Implement and test RAG pipelines integrating LLMs with external data sources. - Perform exploratory data analysis (EDA), preprocessing, and feature extraction. - Collaborate with cross-functional teams to design experiments and validate model outputs. - Document processes, findings, and code for reproducibility and scalability. Required Skills & Qualifications: - Strong proficiency in Python and libraries like Pandas, NumPy, and LangChain / LlamaIndex. - Understanding of Prompt Engineering techniques for LLM optimization. - Hands-on knowledge of RAG (Retrieval-Augmented Generation) implementation. - Familiarity with APIs, vector databases (e.g., FAISS, Pinecone, Chroma), and embeddings. - Good analytical, problem-solving, and communication skills. - Ability to work independently and deliver within timelines.