AI and Risk Data Science intern

Ümumi məlumat This role is a Data Science / AI Intern role focused on Credit Risk and Generative AI. It combines traditional data science and machine learning with modern LLM and RAG technologies in the banking/financial risk domain.

Öhdəliklər

• Support data preparation, cleaning, exploratory analysis, and feature engineering for credit risk models;
• Assist in developing and evaluating statistical and Machine Learning models for credit risk assessment;
• Explore and prototype LLM-based solutions for financial and risk-related use cases;
• Develop and experiment with RAG (Retrieval-Augmented Generation) pipelines using internal documents, policies, procedures, and knowledge bases;
• Work with document ingestion, chunking, embeddings, vector databases, retrieval, and LLM-based generation;
• Evaluate the quality, relevance, factuality, and reliability of LLM/RAG outputs;
• Support the development of AI assistants for credit risk analysis, policy/document search, and knowledge retrieval;
• Perform data analysis to identify patterns, trends, and key credit risk drivers;
• Support model performance monitoring, validation, and documentation;
• Use Python and SQL for data analysis, modeling, and automation;
• Collaborate with Risk, Data Science, AI, and Business teams to translate business requirements into analytical solutions;
• Research new LLM, GenAI, and ML techniques and assess their applicability to Credit Risk.

Tələblər

• Bachelor’s/Master’s student or recent graduate in Data Science, Computer Science, Mathematics, Statistics, Economics, Finance, or a related field;
• Good understanding of Python and SQL;
• Basic knowledge of statistics, probability, and Machine Learning;
• Familiarity with Pandas, NumPy, Scikit-learn;
• Understanding of LLM and Generative AI concepts;
• Familiarity with RAG architecture and concepts such as embeddings, vector search, semantic retrieval, and prompt engineering;
• Strong analytical and problem-solving skills;
• Interest in Credit Risk, Banking, Data Science, AI, and GenAI.