Quantitative Research Associate - Lending Solutions and Risk Modeling

Quantitative Research Associate - Investment Solutions Data, Analytics & Modeling Join a high-impact team shaping the future of lending solutions and the corresponding risk management. Leverage your quantitative expertise to develop innovative models and analytical frameworks to determine collateral requirements for portfolio lending. Be part of a collaborative environment where your insights directly influence lending decisions withing the Private Bank. As a Quantitative Research Associate within the Investment Solutions Data, Analytics & Modeling team, you will develop and enhance analytical tools to estimate Loan to Value (LTV) for capital market securities. You will collaborate with global partners to implement model improvements and ensure robust governance and performance monitoring. Your work will drive data-driven decision-making and support the evolution of Portfolio level Lending Requirements. Job Responsibilities: Design, implement, and enhance quantitative models and frameworks for determining lending value for a portfolio of tradable securities. Propose model enhancements and demonstrate its conceptual soundness via rigorous back-testing. Conduct independent validation and back-testing of existing and new lending value models, ensuring alignment with regulatory standards and internal risk policies Partner with Technology to design and implement cost-effective model changes, and conduct implementation testing. Conduct ad-hoc quantitative research and analysis. Manage multiple priorities in a dynamic professional environment. Required qualifications, capabilities, and skills: Demonstrate 6+ years of experience in risk modeling, quantitative research with focus on Lending, Credit and Market risk. Hold a graduate degree in a quantitative discipline such as Math, Statistics, Finance, Economics, or Engineering. Exhibit knowledge of equity, fixed income, and alternative investment products. Apply strong quantitative modeling and time series analysis skills. Proficiency in programming languages such as Python and SQL, with a willingness to learn new tools. Manage financial data handling, retrieval, and modeling tasks effectively. Communicate complex concepts clearly in both written and verbal formats. Preferred qualifications, capabilities, and skills: Actively pursuing or having completed the CFA or FRM certification. Demonstrate the understanding of lending business (collateral requirement, loan to value estimation, initial and maintenance margins, shortfall monitoring etc. for tradable securities). Adapt quickly to new analytical tools and technologies.