Job Description
As a Quantitative Research Associate within Wholesale Credit Risk group, you will work in the newly formed Counterparty Credit Risk QR team that designs, manages & owns quantitative models and risk limit metrics such as Strategic Stressed Exposure (SSE), Potential Future Exposure (PFE). The team also owns back-testing procedures to control the risk associated with Central Clearing Counterparties (CCP). The mandate of CCR QR team is actively expanding with current scope including -
- Manage enhancements to the SSE framework which governs the computation, scenario design and monitoring as well as the impact quantification of risk drivers not being stressed adequately (Risk not in Stress).
- Developing statistical models and tools for the assessment and management of counterparty credit risk covering CCP related risk.
- Design and implement software framework for counterparty credit risk in Python, delivering results through dashboards.
- Partner with control teams for ongoing model and risk governance.
- Engage tech partners to deploy models to front end solutions.
Your key responsibilities in the role will include:
- Develop, support and enhance the Risk not in Stress (RNIS) framework by identifying & quantifying the impact of the risks not captured in existing stress scenarios.
- Build and manage quantitative risk models for the assessment and management of counterparty credit risk covering exposure cleared by the Central Counterparty (CCP). Additionally, monitor the CCP metrics as disclosed in Public Quantitative Disclosure (PQD).
- Perform & apprise Credit Officers of margin changes across global CCPs for variety of contracts. Close monitoring of margin backtesting in reaction to daily price movements is also key deliverable.
Requirements
- Demonstrable relevant 3-4 years experience in Quantitative Research or Risk Modeling roles with an investment bank or financial institution. Familiarity with counterparty risk domain is required.
- Strong** **educational background in Quantitative discipline such as Master's/Ph.D in Financial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Economics, or related field of study.
- Knowledge of financial instruments like OTC derivatives, Futures & Options, and Securities Financing Transaction (SFTs), along with understanding of risk management methodologies (VAR and stress testing) across all asset classes is highly preferred.
- Substantial programming skills expertise in Python & R. Working knowledge C++ is preferred.
- Familiarity with AI agentic coding would be a plus.
- Strong analytical mindset with excellent problem solving and data interpretation skills.
- Excellent communication skills with ability to verbally & logically articulate complex information. Interpersonal skills will be useful as projects can require interaction & synchronization with other teams.
- Highly organized and can work both independently and as part of a team. Possess a strong risk and control mindset. Detailed oriented but also able to deliver on multiple time sensitive timelines.