Data Scientist Credit Scoring & Analytics Implementation

Role : Data Scientist Credit Scoring & Analytics Implementation Location : Chennai Experience : 8-10 Years CTC :15LPA to 20LPA Notice period : Immediate to 15days only Hands-on experience : Machine Learning model implementation and deployment, SQL, Python. Key Responsibilities Lead the design, development and validation of credit risk scorecard models using ML, AI, and other statistical techniques, using Financial and Alternate Data. Perform advanced exploratory data analysis (EDA), feature engineering, and data preparation on large, complex datasets Translate business and risk requirements into analytical solutions and support their integration into production systems. (e.g., AUC, KS, Gini) Own end-to-end model lifecycle: development, validation, deployment and ongoing monitoring Partner with engineering teams to integrate models into production systems (APIs, batch scoring, real-time decisioning) and collaborate with application teams to ensure robust and scalable implementation of scoring logic Develop monitoring frameworks, dashboards, and reports to track model performance, drift and portfolio health Produce high-quality technical documentation, validation reports, model reports and stakeholder presentations Provide technical guidance, best practices and task allocation to team members and support knowledge transfer across teams. Required Technical Skills Must Have 5–10 years of experience in ML models implementation & credit risk technology solutions Deep understanding of statistical modelling techniques (logistic regression, WOE/IV, binning, model validation) and machine learning methods Strong proficiency in Python (preferred) or similar analytical tools (e.g., SAS, STATA) Strong understanding of .NET / C# based applications and system integration Advanced SQL skills and experience working with large-scale relational databases (e.g., Oracle, SQL Server, Postgres and MongoDB) Experience managing analytics or technology delivery projects Strong communication skills Good to Have Basic understanding of credit risk modelling / scorecard concepts Familiarity with BI and visualization tools such as Power BI Knowledge of regulatory frameworks in credit risk (e.g., IFRS 9, Basel III) Experience with cloud platforms (AWS, Azure, or GCP) Impact Drive credit risk strategy through robust, production-grade models Improve portfolio performance and decision accuracy Shape best practices in model development, deployment, and monitoring.