Consultant/ Sr. Consultant- Credit Risk Quant

Solytics Partners Co; Pune District; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Solytics Partners Co is seeking a skilled Credit Risk Modelling professional to join our risk analytics team in Pune. The role covers development, validation, and enhancement of credit risk models, including regulatory and scorecard-based models, with AI/ML integration.

You will work across Retail and Wholesale portfolios, applying statistical and machine learning methods to improve model performance, while ensuring governance and regulatory compliance.

Qualifications

• 2–5 years of experience in credit risk modelling or risk analytics.
• Strong understanding of credit risk concepts and model lifecycle.
• Hands-on experience with statistical and machine learning techniques.
• Ability to present modelling results to technical and non-technical stakeholders.

Responsibilities

• Support model implementation and productionization, translating methodology into rules.
• Identify model risks, limitations, and improvement opportunities.
• Develop and maintain credit risk models across portfolios (Retail/Wholesale).
• Work with regulatory models (PD, LGD, EAD, IFRS9, Basel) and scorecards.
• Apply statistical and ML techniques to improve performance and power.
• Perform data analysis, feature engineering, and model calibration.
• Conduct validation, backtesting, and performance monitoring.
• Ensure regulatory compliance and governance for models.
• Collaborate with Risk, Business, and Technology teams.
• Prepare model documentation for audits and reviews.

Skills

Credit risk modelling

Risk analytics

Model validation

Statistical learning

Tools

Python

R

SAS

SQL

Job description

Job Summary:

We are looking for a skilled Credit Risk Modelling professional to join our risk analytics team. The role involves development, validation, and enhancement of a wide range of credit risk models, including regulatory, internal, and scorecard-based models, along with integration of AI/ML techniques within the credit risk framework. This is a dynamic role offering exposure across portfolios, methodologies, and model types rather than being limited to a specific model.

Key Responsibilities:

• Support model implementation and productionization, including translating model methodology into technical/business rules where required.
• Identify and communicate model risks, limitations, and improvement opportunities to relevant stakeholders.
• Develop, enhance, and maintain credit risk models across portfolios (Retail / Wholesale).
• Work on a variety of model types including:
• Regulatory models (PD, LGD, EAD, IFRS9, Basel)
• Application / behavioral scorecards
• Apply statistical and machine learning techniques to improve model performance and predictive power.
• Perform data analysis, feature engineering, and model calibration.
• Conduct model validation, backtesting, and performance monitoring.
• Ensure models comply with regulatory requirements and internal governance standards.
• Collaborate with stakeholders across Risk, Business, and Technology teams.
• Prepare model documentation and support audit/regulatory reviews.

Key Requirements:

• 2–5 years of experience in credit risk modelling or risk analytics.
• Strong understanding of credit risk concepts and model lifecycle.
• Experience working on one or more:
• Scorecards (application / behavioral)
• Experience working with one or more portfolios such as Credit Cards, Personal Loans, Mortgages, SME, Corporate, or Commercial Banking etc.
• Exposure to macroeconomic modelling, scenario analysis, stress testing, or forward-looking adjustments is preferred.
• Experience in model implementation, productionization, or translating model methodology into code/business rules is preferred.
• Experience in ongoing model monitoring, threshold setting, trigger analysis, and periodic model performance assessment.
• Understanding of model governance, model documentation, change management, and model lifecycle processes.
• Ability to present modelling results, explain technical concepts, and communicate findings to business and non-technical stakeholders.
• Hands-on experience with statistical and machine learning techniques (e.g., regression, decision trees, random forests, boosting, etc.).
• Proficiency in tools such as Python / R / SAS / SQL.