Position Overview
We are seeking a highly analytical Quantitative Model Developer with strong Python engineering capabilities and deep expertise in cross-margining, prime brokerage, and capital markets.
This role focuses specifically on counterparty credit risk modeling, rather than pricing or market risk models. The successful candidate will develop and enhance cross-margin methodologies, derive mathematical formulas, identify gaps in existing models, and implement rigorous quantitative solutions using Python.
This is a highly technical role requiring a strong combination of mathematics, stochastic modeling, financial products knowledge, and software engineering. The individual will work closely with model owners, business stakeholders, technology teams, project managers, auditors, and junior quantitative professionals.
Because cross-margin exposure can have significant impact on the CIB business, this role requires someone who can respond with a strong sense of urgency, ownership, and responsiveness to high-priority and ad hoc modeling requests.
Key Responsibilities
Quantitative Modeling & Analysis
- Develop, enhance, validate, and maintain counterparty credit risk models supporting cross-margin methodologies.
- Derive mathematical and analytical formulas and translate them into robust quantitative implementations.
- Evaluate modeling assumptions and identify methodological gaps, inconsistencies, and legacy limitations.
- Modernize or replace outdated methodologies using appropriate stochastic, statistical, and capital markets modeling techniques.
- Develop quantitative methodologies across complex financial products, including:
- Equity swaps
- Metals
- Energy derivatives
- Convertible bonds
- Other derivatives and structured products
- Support counterparty exposure methodologies such as Potential Future Exposure (PFE), Expected Exposure (EE), and Exposure at Default (EAD) where applicable.
- Validate model outputs and ensure methodologies are mathematically sound and appropriately documented.
Python & Quantitative Technology Development
- Lead development and enhancement of Python-based quantitative libraries used for model development, analysis, and validation.
- Build well-structured, reusable, testable, and maintainable quantitative code.
- Develop quantitative prototypes and work with technology teams to transition successful solutions into production.
- Leverage AI-assisted development tools such as GitHub Copilot and similar technologies to improve coding efficiency and automation.
- Write and optimize SQL queries to analyze and manipulate large datasets supporting quantitative modeling activities.
- Partner with technology teams on production implementation, testing, and deployment.
Cross-Functional Collaboration
- Work closely with model owners, business partners, technology teams, auditors, project managers, and other quantitative professionals.
- Translate business requirements into clear quantitative methodologies, model specifications, and technical documentation.
- Explain complex mathematical and modeling concepts clearly to technical and non-technical stakeholders.
- Provide coaching and technical guidance to junior team members.
- Participate in model reviews, methodology discussions, implementation reviews, and validation activities.
Operational & Delivery Responsibilities
- Respond rapidly to urgent and high-impact requests associated with cross-margin exposure within the CIB business.
- Manage multiple modeling priorities while maintaining mathematical and implementation quality.
- Deliver model enhancements, prototypes, documentation, and validation materials within required timelines.
- Proactively identify potential modeling or implementation issues before they create downstream risk.
Required Technical Skills
- Expert-level Python development experience, preferably in quantitative or financial applications.
- Experience building and maintaining Python quantitative libraries.
- Strong SQL skills and experience working with large financial datasets.
- Experience with AI-assisted coding tools such as GitHub Copilot or similar.
- Strong numerical programming and quantitative development skills.
- Experience with stochastic modeling, probability, statistics, and capital markets models.
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