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Tier-1 employer, metro location, and mid-level quant title drive high candidate competition.
Specialized deposit balance sheet, ALM and regulatory modeling skills limit cross-industry transferability.
Mandatory 4+ years, regulatory modeling expertise, and Python/R/SAS requirement enforce strict shortlisting.
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Develop, implement, validate, and document deposit balance sheet and treasury analytics models related to asset & liability management and capital planning.
Forecast losses and compute capital requirements by managing market, credit, and operational risks using advanced quantitative methods and statistical theory.
Lead stakeholder engagements for model review, challenge sessions, and compliance with model validation governance involving business units, finance, treasury, regulators, and audit.
Bachelor's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science.
4+ years of quantitative analytics experience, explicitly including experience in deposit balance sheet modeling, treasury analytics, or equivalent.
Proficiency in Python, R, or SAS programming languages is mandatory.
Experience with model life cycle activities (development, monitoring, implementation, forecasting) and strong documentation skills to communicate complex models.
Demonstrated expertise in Deposit and PPNR modeling, asset & liability management, and interest rate risk in banking book (IRRBB) modeling for balance sheets and capital planning.
Experienced in navigating cross-functional stakeholder environments including regulatory, audit, finance, and business lines for model governance and challenges.
Skilled in rapid problem-solving programming challenges in Python/R/SAS with strong project management and ability to deliver under pressure in a complex financial environment.