This role is a Data Science / AI Intern or Junior Data Scientist 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.

The main focus areas are:

• Credit Risk Analytics: Analyzing financial and customer data to identify credit risk drivers, patterns, and trends.
• Data Science & Machine Learning: Preparing data, performing EDA, feature engineering, and building/evaluating statistical and ML models for credit risk assessment.
• Python & SQL: Using Python and SQL extensively for data analysis, automation, modeling, and data preparation.
• Generative AI & LLMs: Exploring how LLMs can be applied to financial and risk-related problems.
• RAG Systems: Building RAG pipelines that allow AI assistants to search and retrieve information from internal documents, policies, procedures, and knowledge bases.
• AI Assistants: Supporting AI tools for credit risk analysis, document search, and knowledge retrieval.
• Model & AI Evaluation: Evaluating ML models and LLM/RAG systems based on performance, relevance, factuality, and reliability.
• Business Collaboration: Working with Risk, Data Science, AI, and Business teams to turn business requirements into analytical and AI solutions.

• 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
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This role can provide strong benefits for someone who wants to build a career at the intersection of Data Science, Banking, AI, and GenAI:

• Real-world Credit Risk experience — You learn how data science is applied to important banking decisions.
• Hands-on Machine Learning experience — You work with real data preparation, feature engineering, model development, validation, and monitoring.
• Generative AI experience — You get practical exposure to LLMs rather than only theoretical knowledge.
• RAG pipeline experience — You can gain experience with embeddings, vector databases, semantic search, retrieval, chunking, and prompt engineering.
• Strong technical skill development — Python, SQL, Pandas, NumPy, Scikit-learn, ML, LLMs, and RAG are all highly relevant skills for the current job market.
• Banking & Finance knowledge — You develop domain expertise in credit risk, which can be valuable for future Data Scientist, Risk Analyst, AI/ML, or Quantitative roles.
• Exposure to production-oriented AI — The role focuses not only on building models but also on evaluating reliability, factuality, documentation, and practical business use cases.
• Cross-functional experience — Working with Risk, AI, Data Science, and Business teams helps develop communication and business problem-solving skills.
• Research & innovation — You will have opportunities to explore new ML and GenAI techniques and assess whether they can solve real business problems.
• Career opportunities — This experience can prepare you for roles such as Data Scientist, ML Engineer, AI Engineer, Credit Risk Analyst, Risk Data Scientist, GenAI Engineer, or Quantitative Analyst.

Key Responsibilities

• 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

Kapital Bank iş mühiti, əlavə fürsətlər və digər vakansiyaları görüntüləmək üçün Kapital Bank Life səhifəsinə keçid edin.

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