The role involves developing mathematical models for the valuation of credit derivatives, structured lending facilities, and illiquid collateral. The individual will implement methodologies for model calibration and manage model risk appetite. The position requires leveraging machine learning and data analytics to enhance valuation methodologies. The professional will define methodologies for pricing adjustments, model limitations, and liquidity reserves. Additionally, the role includes monitoring model performance metrics and designing software frameworks for analytics delivery. Collaboration with trading desks, controllers, and risk teams is essential to safeguard the firm's balance sheet.

The role requires demonstrated quantitative, research, and problem-solving skills alongside a strong understanding of advanced mathematics, including probability theory, stochastic calculus, and statistics. Candidates must possess proficiency in Python and C++, with practical experience in code design, optimization, debugging, and reverse engineering. A deep understanding of financial products and their associated risks is necessary. While not strictly required, an advanced degree such as a PhD or MSc in fields like Engineering, Mathematics, Physics, or Computer Science is preferred, as is six years of experience in quantitative model development or validation.