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Tier-1 employer, metro location, and mid-level role drive moderate applicant competition.
Role demands finance-modeling, risk, and validation experience, limiting cross-industry transferability.
Explicit 2+ years, finance-modeling domain expertise, and mandatory Python/SAS/SQL requirements increase strictness.
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Support and participate in maintaining and optimizing low to moderately complex quantitative models related to operational processes including validation, reporting, and documentation.
Analyze basic data sets and model outputs to validate model efficiency and support business decisions focused on model optimization.
Present recommendations to leadership for resolving issues, collaborate with global teams, and contribute to model operations and testing frameworks.
Minimum 2+ years of experience in quantitative model solutions or quantitative model operations, or equivalent demonstrated through work experience, training, or education.
Bachelor's degree in finance, statistics, computer science, IT, or related field with 2+ years of relevant experience.
Technical skills including Python/R or SAS, Excel, SQL, and data visualization tools like PowerBI/Tableau/Qlikview.
Experience with data engineering, automated testing frameworks (e.g., Pytest/Selenium), and understanding of finance/credit/banking modeling and SDLC processes.
Experienced in finance or credit modeling environments with strong understanding of industry-specific SDLC and model reengineering.
Capable of developing and maintaining automated test scripts and frameworks for data migration, with strong analytical and risk assessment skills.
Able to work independently and collaboratively with global teams, presenting solutions to leadership and partnering with business stakeholders.