U.S. Bank Quantitative Analyst- Counterparty Risk in New York, New York
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U.S. Bank is seeking a Quantitative Analyst to provide oversight over sourcing market and trade data and connecting to new proprietary model. The counterparty risk management function is responsible for the management and control of counterparty risk at US Bank. The group provides independent review of counterparty risk across the business, producing detailed analysis of the derivative portfolio for internal and external constituents and manage the control framework under which counterparty risk is governed.
The person will be responsible for the implementation of in-house counterparty risk model development and related quantitative market data methodologies (standardization, proxies, data cleaning, and operational process). This includes how to measure the quality of data for use in pricing models for Counterparty Models. Employ analytical and quantitative approaches to analyze and process data required for the new model.
Serves as an escalation point in interactions with stakeholders across Front Office and Quantitative Model teams including development of teams and technology on various market data-related topics: Daily market data quantitative analysis, Data quality remediation efforts (e.g., designing quantitative key performance indicators so as to measure quality of data, or, quantitative approaches to fill missing data, working with Front Office desks), Communicates market data quality issues to downstream users
Manages requests for new market data (e.g., participating in new product approval process, interacting with vendor procurement teams). Represents CRM Market Data Quantitative Analysis team in governance forums and senior leadership discussions. Ensures team adheres to firm-wide policies for data governance and controls. Coordinates with all external model vendors and internal quantitative partners around all key risk analytic topics. Works with model development and validation groups in ensuring the accuracy and reasonableness of the stress test and other model assumptions. Partners with them in ensuring on smooth maintenance of ongoing monitoring of models. Review all counterparty related model documentation and sign-off on key assumptions. Liaison with technology and other key groups regarding counterparty risk infrastructure and processes.
Bachelor’s degree in quantitative field, and 10 or more years of experience in counterparty/market risk management OR
Master’s or PhD degree in quantitative field, and 8 or more years of experience in counterparty/market risk management
Experience in capital markets, industry experience within the specific sector of the position, or a combination of both
Master’s degree or higher in a quantitative field such as mathematics, engineering, physics, or statistics
Required strong Python & SQL
Experience financial modeling and strong understanding of stochastic processes
Experience building an efficient large scale and complex market data operations/end to end market data engineering processes in a sales and trading environment
Experience working with quant teams developing market data management systems which allow for efficient data retrieving to optimize modeling approaches
Experience leading large teams/multiple business partners, overseeing business as usual market data operations with strong governance processes and controls
Knowledge and understanding of traded derivative and cash products across all asset classes to effectively have discussions with Front Office traders, Market Risk Officers, and Risk Analytics team
Experience overseeing the development and implementation of quantitative methods for data quality checks and remediation techniques including statistical data filling methods and proxying
Understanding of how market data impacts the downstream uses
Counterparty credit risk and CVA
Understanding of major market data sources (e.g., Bloomberg, Reuters, Markit)
Market data object construction methods for curves and volatility surfaces
Risk factor types across asset classes
Market data related to regulatory requirements
Experience in quantitative or complex data management field
Experience working with large scale and complex data management systems
Proficient in Python and SQL (reading and writing queries)
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