Role Overview

This role will work within the Model development function and will focus on model development, model calibration, implementation and monitoring of risk models across business functions, and development of challenger models, as necessary.

Responsibilities

This role will be expected to work hands-on to build and implement risk models, and bring in domain/quantitative best practices.
Work hands-on in development, redevelopment, and calibration of risk and other models for lending portfolio.
Data and quantitative analysis to support modelling decisions for underwriting, account management and internal rating scorecards.
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.
Develop model performance metrics and a detailed model monitoring plan to ensure continued use of these behavioural models.
Bringing in industry best practices and consultative inputs to help deliver continuous value in advanced risk analytics.

Requirements

1 to 4+ years of relevant experience.
Relevant experience in Banking and Financial services, with experience in predictive modeling of regulatory and nonregulatory credit risk domain
Experience in developing, validating models and risk management of credit risk models.
Knowledge of various statistical techniques and proven skill in regulatory and nonregulatory credit risk modelling.
End-to-end development of credit risk and regulatory models including but not limited to PD, LGD, EAD, Credit Scorecards, Behavioural scorecards etc.
Develop statistical/mathematical and machine learning based models, fine tuning/optimization, testing, reviewing, and performing validation activities and prepare end to end model documentation.
Detailed knowledge of data analysis / analytics / mining techniques.
Excellent knowledge of various statistical techniques.
Strong expertise in Python, SQL, and working with large datasets.
Expertise in Classical ML techniques is a must, experience in Deep Learning is a plus.
Knowledge of model monitoring tech stack (e.g. Kibana, Grafana, Prometheus).
Prior experience in MLOps including building micro-service and deploying the model on AWS, GCP tech stack.