Associate, Quantitative Strategist, Corporate Credit - Loans

Develop and implement quantitative models to support investment strategies across leveraged loans and CLOs. Utilize knowledge of the mechanics of structured products to conduct model development and risk analysis. Apply advanced mathematical and statistical techniques to solve complex problems and make data-driven decisions. Develop and maintain accurate risk financial models and financial models in Python. Work with AWS and databases to manage and analyze large datasets. Disseminate complex quantitative concepts and solutions effectively to stakeholders, including traders, portfolio managers, and risk managers. Collaborate with teams, including technology, risk management, and trading, to integrate quantitative models into broader business processes. Requirements: - Bachelor's degree in a quantitative field such as mathematics, statistics, physics, engineering, computer science, or a related field. - Three years of experience at a sell-side or buy-side financial institution, including two years in credit analysis and two years in leveraged finance. - Proficiency in Python, including coding financial models and bringing code to production, and in structured products. - Experience with programming languages such as Python, R, C++, KDB/Q, MongoDB, SQL, and MATLAB. - Experience with AWS and database management; working with Intex performance and loan-level data. - Experience in machine learning, convex optimization, statistics, time-series analysis, data wrangling and analysis, quantamental analysis, portfolio theory and construction, credit index rebalance analysis, web development, web scraping, credit analysis, cash-flow modeling for securitized products, and ETF fund-flow analysis.