Help strengthen how we measure and manage risk in cleared derivatives. You will build quantitative models and tools that assess central counterparty margin adequacy and support counterparty 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. Job summary As a Quantitative Research Senior Associate in Wholesale Credit Risk Quantitative Research, you will develop models and tools that assess central counterparty margin adequacy and support counterparty 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. Job responsibilities Develop expertise in quantitative topics related to central counterparties 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 counterparty risk frameworks Build statistical models and analytics to assess and manage counterparty 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 Required qualifications, capabilities, and skills 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 Preferred qualifications, capabilities, and skills Proficiency in R in addition to Python Experience assessing central counterparty margin methodologies and margin adequacy Experience developing or enhancing counterparty 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