1. Business Problem Understanding & Approach Development • Engage with Business, Credit, Risk, Marketing, HR, and Audit teams to understand problem statements • Participate in cross-functional discussions to understand processes and identify analytical opportunities • Translate business requirements into structured analytical approaches and solution frameworks ________________________________________ 2. End-to-End Project Ownership • Own delivery of analytics projects from problem definition to implementation and monitoring • Manage timelines, stakeholder expectations, and delivery quality • Ensure solutions are aligned with business objectives and decision-making needs ________________________________________ 3. Data Preparation & Variable Creation • Extract, clean, and prepare data from multiple sources • Perform feature engineering and create relevant variables for model development • Ensure data quality, consistency, and readiness for analysis ________________________________________ 4. Model Development, AI Solutions & Analytical Products • Develop and deploy predictive, prescriptive, machine learning, and AI-driven solutions across customer lifecycle, risk, collections, fraud detection, marketing, and operational use cases. • Design, train, fine-tune, evaluate, and optimize statistical, machine learning, deep learning, and Generative AI models. Build solutions leveraging LLMs, RAG (Retrieval Augmented Generation), Agentic AI frameworks, NLP, and recommendation systems where applicable. • Develop reusable analytical assets, APIs, model endpoints, and AI services for enterprise-wide adoption. • Ensure model explainability, fairness, robustness, scalability, and business relevance. ________________________________________ 5. Business Analysis & Insight Generation • Conduct detailed data analysis to identify trends, patterns, and performance gaps • Generate insights to support decision-making across lifecycle stages • Translate analytical outputs into clear, actionable business recommendations ________________________________________ 6. Model Scoring, Monitoring & MLOps • Perform regular model scoring, performance monitoring, and stability assessments for deployed models. • Monitor model drift, data drift, hallucination risks (for GenAI solutions), and overall model effectiveness. • Establish model performance dashboards, automated alerts, and monitoring frameworks.