Quantitative Analyst specializing in Credit Portfolio Models and Credit Economic Capital

Job Description

Quant Analyst – Credit Portfolio Models | UBS

The Tone:
This is a full-time role at UBS, located in Mumbai. UBS is a leading and truly global wealth manager, the leading universal bank in Switzerland, and also provides diversified asset management solutions and focused investment banking capabilities. This position is vital for advancing the firm’s quantitative methodologies to forecast credit losses for Economic Capital modeling and Stress Testing. The work directly contributes to critical risk management processes, ensuring timely and accurate reporting for internal and external stakeholders.

The TL;DR
• Role: Full Time
• Type: Full Time
• Location: In-person – Mumbai, India(Maharashtra)

• Team: Firmwide Stress Testing Models in Mumbai, part of the group-wide Quantitative Risk Methodology department.
• Mission: Develop and maintain quantitative methodologies for credit loss forecasting in Economic Capital modeling and Stress Testing.
• Tech Stack: R programming

What You’ll Actually Do
• Develop: Develop quantitative methodologies to forecast credit losses for Economic Capital modeling and Stress Testing, crucial for firm-wide risk assessment.
• Design: Design and implement prototypes or automated solutions, primarily in R, while actively maintaining and enhancing existing codebases.
• Collaborate: Collaborate closely with cross-functional teams, including IT, Model Risk Management, and Reporting, to ensure timely and accurate credit risk reporting.
• Produce: Produce and maintain high-quality documentation for both internal and external stakeholders, comprehensively covering methodologies and processes.
• Support: Support the execution of risk processes, including preparing for and contributing to regulatory submissions.

The Must-Haves
• Background: Master’s degree in a quantitative discipline such as Mathematics, Physics, Statistics, Engineering, Econometrics, or Finance.
• Experience: Proven experience in a risk department with quantitative topics, expertise in credit risk methodologies, and practical experience in handling large datasets.
• Skills: Strong analytical, organizational, and problem-solving abilities to meet tight deadlines; programming proficiency, particularly in statistical languages like R; ability to communicate logically and precisely, including writing rigorous and clear mathematical model documentation; very good verbal and written English communication skills for effective global team interaction.
• Bonus: Curiosity to explore how Artificial Intelligence can improve workflow efficiency with sound judgment, alongside an enthusiasm for creating new statistical models.