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Tier-1 bank, mid-level quant role in metro with common ML skillset attracts dense competition.
Strong credit risk and regulatory modeling requirements limit transferability across industries.
Requires specific credit risk, model lifecycle, and regulatory experience plus Python/SAS skills.
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Conduct and analyze model monitoring, including tracking stability, predictive power, portfolio performance, and risks; recommend remediation or enhancements.
Analyze portfolio trends and model results to identify key risk drivers, validate assumptions, and support governance, audit, and regulatory requirements.
Provide analytical support and solutions for business initiatives, improve risk management workflows, and collaborate with stakeholders and regulators.
2+ years of Quantitative Analytics experience or equivalent through work, training, or education.
Bachelor's degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or another quantitative discipline.
Proficiency in Python, R, SAS, C++, and SQL for statistical and mathematical modeling.
Experience in credit risk analytics or related quantitative analytics required; familiarity with credit risk model lifecycle is expected.
Experienced in credit risk analytics with exposure to statistical and machine learning model development and model monitoring.
Strong Python programming skills for complex data manipulation, analytics, and statistical model development.
Ability to interpret data and model outputs to generate insights supporting effective credit risk management and governance.