EY's Financial Services Organization (FSO) Quantitative Advisory Services (QAS) group needs to help clients identify, measure, manage, and monitor various risks, including market, credit, operational, and liquidity risks, as well as asset-liability management, capital management, and other business activities. They also need to leverage advanced analytics, AI/ML, Generative AI, and NLP to provide insights and solutions for complex client challenges in the financial services industry.
Requirements
- Capability to effectively program computer routines using one or more languages such as C++, Java, Python, VBA, SAS, R, and SQL, with a focus on developing algorithms and computational models to solve complex problems.
- Proficiency in credit markets, regulations, and accounting standards related to credit risk, with the ability to develop, apply, build, and calibrate tools and methods associated with credit models that support lending decisions.
- Familiarity with model risk frameworks, model life cycles, and relevant regulations, along with the ability to document risks and models effectively.
- Knowledge of and ability to apply advanced statistics and mathematics to financial modeling, risk assessment, and capital management, informed by business domain knowledge.
- Understanding of computational models, tools, and techniques to interpret data and formulate solutions, with an emphasis on coding modules and algorithms.
- Knowledge of enterprise risk management, including ESG considerations, operational risk, KYC, AML, fraud, and trade surveillance, with the ability to apply relevant modeling techniques and data sources.
- Knowledge of how to leverage firm-approved AI tools in a business setting, including Microsoft Copilot.
Responsibilities
- development, implementation, testing, validation and other quantitative services of analytic models
- risk management including market risk, credit risk (including counterparty credit risk, wholesale, and retail), liquidity / treasury risk, operational risk, compliance risk (including Financial Crime risk), and climate risk
- regulatory capital analytics including Basel III / Fundamental Review of the Trading Book (FRTB), and Comprehensive Capital Analysis and Review (CCAR)
- front office derivative pricing across various asset classes (e.g., interest rate, foreign exchange, equity, credit, commodity and structured products)
- banking book credit analytics, including loss forecasting, reserves/CECL, and PPNR (Pre-Provision Net Revenue)
- leverages advanced analytics applications, featuring Artificial Intelligence / Machine Learning (AI / ML), including Generative AI (GenAI), and Natural Language Processing (NLP) capabilities
Other
- A strong academic record; currently pursuing a PhD in Computational / Quantitative Finance, Mathematics, Engineering, Statistics, Economics, Physics, Computer Science, or a related field; OR a Master’s degree in the same fields with 2+ years of relevant full-time quantitative analytical work experience in mature financial markets.
- The ability to work collaboratively in a team environment, demonstrate inclusivity, and embrace diverse perspectives, particularly in cross-functional teams focused on credit modeling and risk management.
- Excellent presentation skills and the ability to communicate complex concepts effectively to all audiences, including the documentation of risks and models in compliance with regulatory standards.
- Intellectual curiosity, the ability to take initiative and drive execution, with advanced analytical and problem-solving skills within a dynamic and evolving business landscape, particularly in the context of credit risk and model risk management.
- Exhibits an agile and growth-oriented mindset, with a willingness to adapt to new methodologies and industry practices related to credit and risk modeling.
