AVP/VP, Quantitative Strategist, Structured Product Group

GIC Private Limited•New York, NY

1d•$170,000 - $270,000•Hybrid

About The Position

We are seeking a Quantitative Strategist to join our Structured Product Group, focusing on Agency and Non-Agency securitized products across residential, consumer, and commercial sectors. The role supports portfolio managers and investment teams through quantitative analysis, modelling, data workflows, and tools used for deal evaluation, portfolio monitoring, stress testing, collateral analysis, and relative value assessment. The successful candidate should combine structured-products knowledge, strong coding ability, practical data-management skills, and the judgement to translate analytical work into investment-relevant insights.

Requirements

4-8 years of relevant experience in quantitative/structured-products roles, including direct experience developing or working with residential mortgage prepayment models.
Bachelor's or master's degree in a quantitative field such as Mathematics, Statistics, Physics, Engineering, Computer Science, Financial Engineering, Economics, or a related field.
Strong programming skills in Python, with the ability to write clean, reliable, and maintainable code.
Experience handling large datasets, databases, APIs, cloud-based data platforms, and data-quality controls, including familiarity with tools such as AWS, Databricks, or agentic workflow tools.
Solid understanding of fixed income and structured-products concepts, including cashflows, spread, OAS, duration, convexity, prepayment risk, credit risk, and stress testing.
Familiarity with Agency and Non-Agency securitized products across residential, consumer, or commercial sectors.
Ability to perform cashflow modelling, collateral analysis, sensitivity analysis, and scenario analysis.
Understanding of the sensitivity of structured products and portfolios to macro factors such as interest rates.
Understanding of how to measure liquidity, including TRACE trading volume, outstanding amount, and other liquidity indicators.
Strong problem-solving skills, with the ability to debug data or model issues independently and propose practical solutions.
Excellent communication skills, with the ability to translate quantitative analysis into investment-relevant insights and explain complex quantitative and technical concepts to non-technical stakeholders.
Resourceful, self-directed, and comfortable working across teams and global locations.

Nice To Haves

Direct experience building or validating agency mortgage prepayment models (e.g., logistic, hierarchical, structural, or mixed-effects approaches), and experience with Non-Agency RMBS, ABS, CMBS, securitized-products cashflow models, or structured credit analytics.
Exposure to leading mortgage/structured-products analytics and modelling platforms, whether from the buy side or from a vendor modelling team, such as Intex, Bloomberg (BAM), Yield Book, or BRS/Aladdin.
Experience working with loan-level mortgage data from providers such as CoreLogic (Loan Performance), Black Knight/ICE McDash, or eMBS.
Experience building production or semi-production analytics, dashboards, data pipelines, or investment tools.
Experience with collateral stratification, deal tape analysis, waterfall modelling, or scenario engines.
Interest in applying automation, AI, or modern data tools to investment research, collateral surveillance, and workflow improvement.

Responsibilities

Understand the mechanics and risk drivers of structured products, including cashflows, prepayments, credit performance, collateral characteristics, duration, convexity, and spread behaviour.
Build, calibrate, and validate prepayment models for Agency and Non-Agency residential mortgages, developing insight into borrower, servicer, and lender behaviour, and explaining variance between empirical and modelled prepayment performance in terms of underlying market and policy drivers.
Apply quantitative and statistical methods to support valuation, OAS analysis, scenario analysis, stress testing, and relative value assessment.
Develop and maintain Python-based models and analytical tools for cashflow analysis, portfolio monitoring, deal evaluation, and risk diagnostics.
Analyse loan-level and collateral data, including pool characteristics, vintage performance, issuer behaviour, delinquency trends, and other drivers of securitized-products performance.
Work with large and complex datasets from internal systems, market data and analytics platforms (e.g., Intex, Bloomberg, Yield Book), loan-level data providers, servicers, and external research sources; build repeatable workflows and data-quality checks.
Explain model outputs, assumptions, limitations, risk drivers, and investment implications clearly to portfolio managers, analysts, risk managers, and technology partners.
Partner with investment, quantitative, data, and technology teams to improve analytics, production reliability, workflow automation, and decision support across global locations.