Responsibilities

You will be working with the model development function of a large banking client and will focus on data validation, model methodology, calibration, implementation and monitoring of risk and regulatory models across business functions, and development of challenger models, as necessary. It will also involve interaction with various stakeholder groups including model validation, model owners/lines of business, auditors, and client model developers. You will be expected to work hands-on to assess models, build and lead development teams, and bring in thought leadership and domain/quantitative best practices to present an effective challenge to the models. Your activities will include, but will not be limited to the following:

·         Work hands-on in development, re-development, and calibration of risk and regulatory models for banking book including deposit runoff, average balance forecasting, prepayment models for assets portfolio

·         Data and quantitative analysis to support modelling decisions and ensure the correctness of the market data sourced from several vendors.

·         Work on the development of model methodologies, algorithms, and diagnostic tools for testing model robustness, sensitivity, and stability.

·         Detailing of model techniques and interpretation of variables used in the models to be documented and presented to client stakeholders.

·         Develop model performance metrics and a detailed model monitoring plan to ensure continued use of these behavioural models

·         Development of benchmark models may be required

·         Help develop thorough technical documents for distribution and presentation to senior management, model developers, auditors, and regulators.

·         Bringing in industry best practices and consultative inputs to help deliver continuous value to client engagements in advanced risk analytics.

Minimum Qualifications

·         Educational qualification: master's and bachelor's degrees in quantitative disciplines, like, mathematics, statistics, economics, computer science and engineering, etc.; MBAs must have bachelor’s degree in above-mentioned disciplines

·         Relevant experience:

o   For Assistant Manager: relevant years' experience in Banking and Financial services, with experience in predictive modeling of regulatory and non-regulatory credit risk domain

o   For Manager: relevant years’ experience in Banking and Financial services, with experience in predictive modeling of regulatory and non-regulatory credit risk domain

·         Hands-on experience in SAS is needed. It is good to have Python coding experience.

·         Experience in developing, validating models and risk management of credit risk models.

·         Knowledge of various statistical techniques and proven skill in regulatory and non-regulatory credit risk modelling

·         Understanding and experience of the regulatory risk model development/validation guidelines – SR 11-7, Basel IRB, CCAR, CECL, IFRS9 etc.

·         End-to-end development or independent validation of credit risk and regulatory models including but not limited to – PD, LGD, EAD, Stress Testing, CECL, Credit Scorecards, AML and counter fraud models etc.

·         Develop statistical/mathematical and machine learning based models, rule fine tuning/optimization, testing, reviewing, and performing validation activities and prepare end to end model documentation.

·         Detailed knowledge of data analysis / analytics / mining techniques

·         Hands on expertise in SQL, ETL, SAS, Python, R working with large data sets.

·         Excellent knowledge of various statistical techniques

·         Strong client management and communication/presentation skills – written & verbal.

·         Self-driven, proactive, “can-do” attitude. Ability to work under ambiguity and with minimal supervision.

Preferred Qualifications

·         Strong networking, negotiation and influencing skills.

·         Knowledge of credit risk management for retail and wholesale lending products

·         Hands on experience in Machine Learning modeling techniques