Associate Manager - Credit Scoring & Analytics Implementation

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

• 510 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)
• 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.