General Description

Popular is seeking a Quantitative Analyst to support independent validation of quantitative models in accordance with the Model Risk Management framework and regulatory guidance (e.g., SR 26-2).

The role is responsible for evaluating models used across risk and finance functions, including credit risk, operational risk, stress testing, CECL/allowance estimation, and macroeconomic forecasting, to ensure conceptual soundness, implementation accuracy, and ongoing performance monitoring.

The position requires strong analytical, technical, and communication skills to effectively challenge model assumptions and communicate validation results to stakeholders.

Essential Duties and Responsibilities

• Perform independent model validations covering conceptual soundness, process verification, data quality assessment, outcome analysis, and ongoing performance monitoring.
• Conduct effective challenge of model methodologies, assumptions, limitations, and intended use, while evaluating models supporting credit risk, stress testing, scenario analysis, revenue forecasting, and macroeconomic modeling in alignment with regulatory expectations and internal standards.
• Develop and execute quantitative testing frameworks, including sensitivity testing analysis, back-testing, benchmarking, and robustness testing.
• Produce high-quality validation deliverables, including validation reports, executive summaries, technical documentation, and monitoring frameworks.
• Assess model implementation and production controls, including data lineage, execution environments, and governance processes.
• Collaborate with model developers, model owners, model users, risk teams, internal audit, regulators, and external consultants.
• Support regulatory examinations and internal audits by providing clear, well-documented, and defensible validation work.
• Stay current with evolving regulatory requirements, industry best practices, and emerging methodologies, including AI/ML model governance and explainability.

Education

Bachelor’s or Master’s degree in Computer Science, Mathematics, Applied Statistics, Data Science, Physics or a related quantitative field.

Experience

• At least two years of experience in model development, implementation, or validation within financial services or a quantitative environment.
• Experience in machine learning models is desirable.

Other Qualifications

• Strong foundation in statistical modeling and machine learning techniques, including regression techniques, Random Forest, Gradient Boosting, XGBoost, neural networks, and logistic regression.
• Experience applying model validation techniques, including back-testing, benchmarking, sensitivity analysis, and performance diagnostics.
• Proficiency in Python, R, SAS, or similar statistical tools, as well as SQL and relational databases.
• Familiarity with data ana analytics platforms such as Jupyter, AWS, Power BI, or similar tools.
• Strong analytical and problem-solving skills, with the ability to manage multiple priorities and meet deadlines.
• High attention to detail and ability to produce high-quality documentation.
• Ability to work independently and collaboratively in a team environment.
• Strong interpersonal, influencing, and stakeholder management skills.

• Communication Skills

  • Excellent written and verbal communication skills in English and Spanish.
  • Ability to translate complex quantitative concepts into clear, concise, and business-friendly language.
  • Strong documentation and presentation skills suitable for management.

Region Locations

Popular Center, 014.

Values

Additional Requirements