Roles and Responsibilities :

- Develop credit risk models using SAS, SQL, and statistical modeling techniques to predict defaults, losses, and other credit-related metrics.

- Collaborate with cross-functional teams to design and implement effective credit risk strategies that meet business objectives.

- Conduct stress testing and scenario analysis to identify potential risks and opportunities for growth.

- Provide data insights and recommendations to stakeholders on loss forecasting, scorecards, and portfolio performance.

Desired Candidate Profile :

- 2+ years of experience in Credit Risk Modelling/Analytics or related field.

- Strong expertise in Basel II/III regulations, CECL/CCAR requirements under IFRS9 framework.

- Proficiency in programming languages such as Python/R/SAS; strong understanding of machine learning algorithms an added advantage.

- Demonstrated proficiency in statistical modeling and machine learning techniques using Python and SAS to solve complex credit risk challenges.

- Strong technical command of SQL for handling large-scale datasets and performing advanced data wrangling.

- Proven ability to communicate technical findings to non-technical stakeholders, ensuring alignment on risk strategies and business objectives.

- Exceptional analytical mindset with a focus on delivering precise, scalable solutions in a collaborative, hybrid work environment.

- A solid academic foundation in Statistics, Mathematics, Economics, or a related quantitative field, complemented by 3 to 8 years of relevant experience in the financial services or analytics domain.