•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.
Uyğunluğunuzu görün
Daxil olun və CV-nizi yükləyin, AI bu elana uyğunluğunuzu analiz edib məsləhət versin.
•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 fieldGood understanding of Python and SQLBasic knowledge of statistics, probability, and Machine LearningFamiliarity with Pandas, NumPy, Scikit-learnUnderstanding of LLM and Generative AI conceptsFamiliarity with RAG architecture and concepts such as embeddings, vector search, semantic retrieval, and prompt engineeringStrong analytical and problem-solving skillsInterest in Credit Risk, Banking, Data Science, AI, and GenAI + ' ' + 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 ResponsibilitiesSupport data preparation, cleaning, exploratory analysis, and feature engineering for credit risk modelsAssist in developing and evaluating statistical and Machine Learning models for credit risk assessmentExplore and prototype LLM-based solutions for financial and risk-related use casesDevelop and experiment with RAG (Retrieval-Augmented Generation) pipelines using internal documents, policies, procedures, and knowledge basesWork with document ingestion, chunking, embeddings, vector databases, retrieval, and LLM-based generationEvaluate the quality, relevance, factuality, and reliability of LLM/RAG outputsSupport the development of AI assistants for credit risk analysis, policy/document search, and knowledge retrievalPerform data analysis to identify patterns, trends, and key credit risk driversSupport model performance monitoring, validation, and documentationUse Python and SQL for data analysis, modeling, and automationCollaborate with Risk, Data Science, AI, and Business teams to translate business requirements into analytical solutionsResearch new LLM, GenAI, and ML techniques and assess their applicability to Credit RiskKapital Bank iş mühiti, əlavə fürsətlər və digər vakansiyaları görüntüləmək üçün Kapital Bank Life səhifəsinə keçid edin. Vakansiyalardan daha tez xəbərdar olmaq üçün Telegram kanalımıza abunə olun!