Wholesale Credit Quantitative Research - Senior Associate

Help strengthen how we measure and manage risk in cleared derivatives. You will build quantitative models and tools that assess central counter party margin adequacy and support counter party credit risk management. Working with partners across controls and technology, you will take research into practical, production-ready solutions. Your work will directly inform risk frameworks and governance.

As a Quantitative Research Senior Associate in Wholesale Credit Risk Quantitative Research, you will develop models and tools that assess central counter party margin adequacy and support counter party credit risk management for cleared derivatives. You will collaborate with a team that values strong partnerships, thoughtful analysis, and clear communication. You will work closely with risk governance and control partners to support a well-managed model lifecycle.

You will engage technology partners to help deliver scalable, production-ready solutions.

• Develop expertise in quantitative topics related to central counter parties and cleared derivatives
• Create models and tools to assess the adequacy of margin requirements for cleared derivatives
• Develop and enhance models and toolsets that evaluate the effectiveness of counter party risk frameworks
• Build statistical models and analytics to assess and manage counter party credit risk
• Partner with risk governance and control teams to support model oversight and ongoing reviews
• Collaborate with technology partners to implement, test, and deploy production-ready models and tools
• Document assumptions, methodologies, and limitations clearly to support transparency and re-use

• Communicate findings and recommendations in a clear, logical way to technical and non-technical stakeholders

• Doctorate or master’s degree (or equivalent) in financial engineering, operations research, statistics, mathematics, computer science, economics, or a related field
• 3 years of experience in quantitative research, quantitative strategy, or a closely related quantitative role
• Proficiency in Python for model development and data analysis
• Strong understanding of cleared derivatives and risk management methodologies, including value at risk and stress testing, across asset classes
• Excellent verbal and written communication skills, with the ability to articulate analysis clearly and logically
• Demonstrated attention to detail and the ability to deliver across multiple time-sensitive timelines
• Strong risk and control mindset and a track record of effective cross-team partnership

• Proficiency in R in addition to Python
• Experience assessing central counter party margin methodologies and margin adequacy
• Experience developing or enhancing counter party credit risk models for derivatives
• Experience deploying analytical models into production environments in partnership with engineers
  
  Familiarity with model governance expectations, documentation, and ongoing monitoring practices
• Experience working with cleared products across multiple asset classes