Technical Skills Summary

Modeling PD, LGD, EAD Development, Scorecard Development,

Programming SAS , Python , R, SQL

Regulatory Frameworks IRB Approach (Must-have),

Technical Expertise Advanced Statistical Analysis, Data Preprocessing, Machine Learning

Location: Pune / Mumbai

Experience: 10+ Years

Employment Type: Full-Time

Job Overview

We are looking for a seasoned and strategic Lead Model Developer to spearhead our advanced credit risk modeling initiatives. This role is critical in developing, validating, and implementing sophisticated models for our wholesale portfolios (including Corporate, Banks, Specialized Lending, and Real Estate). The ideal candidate is a subject matter expert who combines deep statistical acumen with practical industry knowledge to drive regulatory compliance and business intelligence.

Key Responsibilities

- End-to-End Model Development: Lead the entire lifecycle of credit risk models (PD, LGD, EAD) for wholesale portfolios, ensuring adherence to Internal Ratings-Based (IRB) standards.

- Data Management & Analysis: Perform comprehensive data sourcing, preparation, and preprocessing using advanced programming tools to ensure data integrity and suitability for modeling.

- Model Implementation & Testing: Design, build, calibrate, and rigorously test models, overseeing User Acceptance Testing (UAT) to ensure seamless implementation into production systems.

- Stakeholder Collaboration: Partner with key stakeholders across Risk, Business, IT, and Validation teams to interpret model outputs, explain technical concepts, and influence data-driven decision-making.

- Documentation & Compliance: Develop and maintain robust, clear, and comprehensive documentation, including Model Development Documents (MDDs), Business Requirements Documents (BRDs), and responses to regulatory and validation queries.

- Mentorship & Leadership: Provide technical guidance and mentorship to junior modelers, fostering a culture of excellence, continuous learning, and professional growth within the team.

- Innovation & Optimization: Continuously research and implement cutting-edge modeling techniques, including machine learning, to enhance model performance, predictive power, and efficiency.

Must-Have Skills and Qualifications

- A minimum of 10 years of hands-on experience in developing and implementing credit risk models within a financial institution or consulting environment.

- Proven, deep expertise in wholesale credit risk modeling is non-negotiable. This must include significant hands-on experience with Corporate, Institutional, and/or Specialized Lending portfolios.

- Advanced Programming Proficiency: Expert-level command of SAS and Python and/or R for statistical modeling, data manipulation, and analysis.

- Strong SQL skills for complex data extraction and querying from large databases.

- In-depth knowledge and practical experience with the Internal Ratings-Based (IRB) approach for regulatory capital calculation (Basel II/III/IV).

- Exceptional understanding of advanced statistical techniques used in risk modeling (e.g., regression, time-to-event analysis, clustering, segmentation).

- Superior analytical, problem-solving, and critical thinking skills with a meticulous attention to detail.

- Excellent written and verbal communication skills, with a proven ability to translate complex technical results into clear, actionable business insights for non-technical audiences.

Preferred Qualifications

- Advanced degree (Masters or Ph.D.) in a quantitative field such as Statistics, Mathematics, Economics, Econometrics, or Finance.

- Practical knowledge of IFRS 9 and/or CECL expected credit loss accounting frameworks.

- Professional certifications like FRM (Financial Risk Manager) or PRM (Professional Risk Manager).

- Hands-on experience applying Machine Learning techniques (e.g., Gradient Boosting, Random Forests) to credit risk problems.

- Familiarity with cloud platforms (AWS, Azure, GCP) and big data technologies (Hadoop, Spark).