Help shape how a global bank manages interest rate risk. In this role, you will build and support models that quantify balance sheet sensitivity to rate and volatility moves. You will partner closely with risk managers, Treasury stakeholders, and technology teams to deliver production-quality analytics that drive decisions.
As a Vice President Quantitative Researcher in the Asset and Liability Management Quantitative Research team, you will build and support interest rate risk and pricing models on the firm’s quantitative analytics platform, with a primary focus on deposits, callable instruments, municipal securities, swaptions, and linear rates. You will own the modeling of behavioral and optionality features across the banking book, calibrate the interest rate and volatility models that underpin our analytics, and ensure models meet internal risk and accounting requirements. You will partner closely with risk managers, Treasury stakeholders, technology, and model risk governance to deliver robust analytics with measurable business impact. This is a banking-book interest rate risk role focused on asset and liability management and structural rate exposure, not trading-desk margin or clearing.
Job responsibilities
- Build and support banking-book interest rate risk and pricing models on the firm’s quantitative analytics platform.
- Model deposits and other behavioral products, including optionality and prepayment/call behavior.
- Develop and enhance models for callable debt, callable preferred instruments, municipal securities, and insurance-related balance sheet products.
- Calibrate interest rate and volatility models used across the asset and liability management portfolio.
- Apply and extend existing quantitative libraries where appropriate, and build bespoke models when needed.
- Ensure models reflect internal risk measurement requirements and applicable accounting frameworks (including available-for-sale and hedge accounting).
- Serve as model owner/developer by managing documentation, controls, monitoring, and change management.
- Respond to model risk, audit, and regulatory reviews with clear explanations of assumptions and limitations.
- Deliver performant, production-quality analytics using strong engineering practices (testing, reproducibility, and continuous delivery).
- Communicate complex quantitative concepts clearly to non-technical stakeholders and drive alignment across partners.
- Provide technical leadership through code and method reviews and mentorship across the team.
Required qualifications, capabilities, and skills
- Advanced degree (PhD, MSc, or equivalent) in Mathematics, Physics, Statistics, Computer Science, or a related quantitative field.
- Demonstrated quantitative research/development experience in interest rate modeling, asset and liability management, or fixed-income derivatives, with production delivery experience.
- Hands-on experience developing on the Athena quant platform (hard requirement).
- Hands-on experience developing on the firm’s strategic quantitative analytics platform.
- Strong knowledge of interest rate products and models, including linear rates, swaptions, callable products, curve construction, and volatility modeling.
- Understanding of banking-book interest rate risk and asset and liability management concepts.
- Strong Python programming skills, including building maintainable, testable, and performant quantitative code and applying numerical methods.
- Strong quantitative problem-solving skills, including the ability to decompose ambiguous problems and communicate uncertainty and trade-offs clearly.
- Strong communication and stakeholder management skills across quantitative, risk, Treasury, and technology partners.
Preferred qualifications, capabilities and skills
- Expertise in curve building (including multi-curve frameworks), volatility modeling and calibration (for example, SABR and short-rate models), and numerical methods (finite-difference methods and Monte Carlo).
- Experience modeling deposits and other behavioral products, callable debt, callable municipal securities, and insurance-related balance sheet products.
- Familiarity with hedge accounting and internal risk measurement frameworks relevant to asset and liability management.
- Experience with model risk governance and regulatory expectations for pricing and risk models.
- Familiarity with data tooling (SQL), performance engineering for large-scale simulations, and applied machine learning techniques in quantitative finance.