Analytics Model Validation

Key Responsibilities:

• Retail Credit Risk Model Management and Validation:

• Lead the validation of complex underwriting, collection, and ECL models independently.

• Perform comprehensive model performance analysis and drive the investigation of model and portfolio variances.

• Ensure that all model validation procedures meet the organizations risk assessment standards.

• Advanced Data Analysis and Reporting:

• Oversee the preparation and consolidation of key risk analysis reports and presentations for senior management committees.

• Automate and streamline reporting processes to generate actionable insights and drive value across stakeholders.

• Ensure the accuracy and relevance of all reports and dashboards, contributing to improved decision-making.

• Strategic Risk Insights and Advisory:

• Lead the deep-dive investigations into unusual model behaviours and portfolio performance discrepancies, advising the business on necessary actions.

• Use advanced analytics to ensure the effective execution of risk analysis and provide strategic insights to the business.

• Stakeholder Engagement and Technical Leadership:

• Act as a technical lead in collaborating with senior stakeholders to ensure model validation and risk management procedures align with business objectives.

• Provide expert guidance to junior team members, ensuring the team adheres to best practices in model validation and risk analytics.

• Leadership in Team Development and Culture Building:

• Champion the development of a purpose-driven, high-performance culture within the team.

• Lead the training and mentoring of junior associates, supporting their professional growth and technical expertise.

• Contribute to strategic decision-making, ensuring alignment between credit risk models and business growth.

Eligibility Criteria for the Job

Education Masters Degree in Statistics, Economics, Mathematics, Engineering; MBA

Experience 6 to 9 Years of relevant experience

Skills & Competencies

• Strong expertise in coding with Python, SQL, and optionally SAS..
• Advanced knowledge of MS-Office Suite, with expert skills in data analysis
• and visualization tools.
• • In-depth experience in analysing and validating credit risk scorecards and
• bureau data.
• • Strong capability in investigating and analysing significant variances in MI and
• model performance.
• • Expertise in advanced machine learning models like Gradient Boosting,
• Neural Networks, Random Forest, etc. is highly preferred.
• • Strong understanding of Data Warehouse, Cloud Data platforms, and their
• integration with model validation.
• • Strong leadership and project management skills, with the ability to influence
• and guide team members and business stakeholders.