A Data Scientist with banking experience designs predictive models, analyzes financial data, and develops ML/NLP solutions for risk management, fraud detection, and customer analytics. Key responsibilities include building credit risk scorecards, automating data pipelines, and ensuring regulatory compliance, typically requiring 3–5+ years of experience with Python, SQL, and statistical modeling in financial institutions. Key Responsibilities \* Predictive Modeling & Analytics: Develop behavioural segments, credit risk scorecards, and predictive models for customer onboarding, cross-selling, and churn retention. \* Fraud & Risk Management: Utilize advanced analytics to identify anomalies and fraudulent activities in transaction data. Implement risk models (probability of default) and maintain regulatory compliance. \* Data Handling: Extract, clean, and analyze structured and unstructured data from internal/external sources. \* Technology & Tools: Write advanced SQL queries and Python/R scripts for data manipulation and build machine learning algorithms (e.g., Scikit-learn, TensorFlow). \* Stakeholder Communication: Translate complex analytical findings into actionable business insights for management. Required Experience & Skills \* Domain Expertise: 3+ years of experience in banking or financial services, specifically in credit risk, fraud strategy, or compliance. Need candidates with 3–8 years’ experience in GenAI/Data Science, strong in Python, LLMs (RAG/fine-tuning), NLP, ML model deployment (MLOps), and cloud (AWS/Azure/GCP), with proven delivery in enterprise use cases