Company Overview:

Our client is a leading global financial services firm providing a wide range of banking, investment and wealth management services to corporations, institutions and individuals. Operating across multiple continents, they leverage cutting-edge technology and data-driven insights to manage risk, optimize performance, and deliver innovative solutions to their clients. Their Bangalore office serves as a critical hub for risk management and analytics, supporting global operations.

Role Overview:

As a Data Scientist within the Credit Risk team, you will be responsible for developing and validating statistical models to assess and manage credit risk across various portfolios. You will collaborate closely with risk managers, model validators, and technology teams to build, implement, and monitor models for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Your work will directly contribute to the firm's ability to make informed lending decisions, manage capital effectively, and comply with regulatory requirements.

Key Responsibilities:

- Develop and implement statistical models for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) to enhance credit risk assessment.

- Conduct model validation and performance monitoring to ensure accuracy, stability, and compliance with regulatory standards.

- Perform data analysis and feature engineering to identify key risk drivers and improve model predictive power.

- Collaborate with risk managers and business stakeholders to understand business needs and translate them into effective modeling solutions.

- Document model development and validation processes to ensure transparency and reproducibility.

- Contribute to the development of risk management strategies and policies to mitigate credit risk exposures.

- Present model findings and recommendations to senior management and regulatory bodies to support decision-making.

Required Skillset:

Responsibilities:o Developing/Reviewing ICAAP & RAROC Models - Demonstrated expertise in developing and validating credit risk models, including PD, LGD, and EAD.

- Strong understanding of statistical modeling techniques, such as regression, time series analysis, and machine learning.

- Proficiency in data modeling, data manipulation, and data visualization using tools like Python, R, and SQL.

- Experience with model validation methodologies and regulatory requirements for credit risk modeling (e.g., Basel Accords).

- Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences.

- Ability to work independently and collaboratively in a fast-paced environment.

- A Master's or PhD degree in a quantitative field such as Statistics, Mathematics, Economics, or a related discipline.

- 3-9 years of relevant experience in risk modeling and analytics within the financial services industry.