Assistant Vice President – Retail Risk Analytics, Risk Management

This is a fully remote position, open to applicants in India.

📋 Description

• Accountable for the design, execution, and upkeep of credit risk models and scorecards, which encompass Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) throughout the retail portfolio lifecycle (acquisition, behavioral, collections).

• Spearhead the development and enhancement of credit risk modeling frameworks by integrating scorecards and relevant statistical/analytical methodologies to aid underwriting and portfolio management decisions.

• Oversee, document, and communicate the performance, assumptions, and limitations of credit risk models to stakeholders, ensuring clarity and interpretability of models.

• Conduct model monitoring, backtesting, and regular recalibration to guarantee models remain precise, stable, and compliant over time.

• Offer suggestions for model redevelopment or enhancement based on portfolio trends, data drift, and new risk patterns.

• Prepare and assist with Basel regulatory reporting, including Risk-Weighted Asset (RWA) estimation and model-related submissions in accordance with internal and regulatory standards.

• Lead or assist in IFRS 9 Expected Credit Loss (ECL) modeling, covering staging, macroeconomic overlays, scenario-based expected credit loss estimation, and stress testing, including climate risk considerations.

• Implement credit risk models into production systems/rating platforms while collaborating closely with IT and data teams to ensure data integrity and system resilience.

• Aid in the design and execution of credit risk strategies and decision rules (e.g., cut-offs, risk segmentation, line management) that align with model outputs.

• Conduct and oversee model validation and testing activities (functional, statistical, and regulatory) prior to deployment.

• Establish solid model governance practices, encompassing documentation, audit trails, and compliance with regulatory standards.

• Recognize opportunities to enhance credit risk models using advanced analytics or machine learning techniques, where suitable and justified.

• Set MLOps standards for model deployment, monitoring, versioning, and performance tracking within production environments.

• Generate data-driven insights to oversee portfolio quality, risk trends, and early warning indicators.

• Collaborate with policy, finance, and business teams to facilitate portfolio optimization, provisioning, and capital management decisions.

• Ensure prompt communication of model performance, validation results, and risk insights to senior management and committees.

• Mentor junior analysts and contribute to the development of a robust, technically proficient credit risk modeling team.

⛳️ Requirements

• 10–12+ years of experience in credit risk modeling within retail banking/financial services.

• Extensive expertise in statistical modeling, machine learning techniques, and large-scale data analysis.

• Strong proficiency in credit risk modeling techniques, including PD, LGD, EAD, scorecards, and segmentation methods.

• Established experience in IFRS 9 ECL modeling and Basel frameworks.

• Solid understanding of the model lifecycle management (development, validation, deployment, monitoring).

• Advanced technical abilities in SAS, SQL, and Python/R.

• Experience working with large datasets and data platforms (e.g., Hadoop or equivalent).

• Strong statistical and analytical capabilities with a talent for translating data into actionable insights.

• Proven history of building, deploying, and maintaining production machine learning models with real-time or near-real-time decision-making systems.

• Familiarity with decision systems/rule engines is an advantage.

• Professional certifications such as FRM (Financial Risk Manager) or CFA (Chartered Financial Analyst) are highly desirable.

• A degree in quantitative disciplines (Statistics/Mathematics/Actuarial Science/Economics).

🏝️ Benefits

• Health insurance.

• 401(k) matching.

• Flexible work hours.

• Paid time off.

• Professional development opportunities.