General Description Popular, Inc. is seeking a highly skilled and motivated Quantitative Analyst to join our Model Risk Management Unit. The successful candidate will play a critical role in validating and monitoring models that support our risk management framework. This position requires a deep understanding of statistical modeling, machine learning, and data analysis techniques. Essential Duties and Responsibilities Validates, tests, documents, implements, and/or oversees usage of advanced quantitative/statistical models for risk management, including credit risk, market risk, and operational risk. Ensure models are robust, accurate, and compliant with regulatory standards. Perform data analysis to support model development and validation, including data cleaning, feature engineering, and exploratory data analysis. Utilize advanced statistical techniques and machine learning algorithms to extract insights from large data sets. Ensure models comply with regulatory requirements and internal policies, including the FRB Supervisory Guidance SR 11-7. Stay updated with the latest regulatory guidelines and industry best practices. Prepare comprehensive model validation reports and documents such as: presentation, written reports, model or reporting code documentation, business requirements and procedures. Ensure documentation is clear, detailed, and accessible to both technical and non-technical stakeholders. Provide effective challenge on conceptual and technical soundness of the models’ design, theory, and framework through various testing following guidelines based on SR 11-7. Perform complex mathematical analysis utilizing various statistical methods or techniques. Areas of focus are models using machine learning (Random Forest, GBT, XGBoost, Neural Networks), logistic regression and various ensemble techniques. Work closely with cross-functional teams, including risk management, compliance, and IT, to ensure models are effectively integrated into business processes. Collaborate with other quantitative analysts or data scientists to share knowledge and best practices. Stay up-to-date with the latest developments in data science, machine learning, and risk management to continuously improve model performance and robustness. Participate in training and development opportunities to enhance skills and knowledge. Qualifications Master’s in Data Science, Statistics, Mathematics, Computer Science, Physics, or a related field. Minimum of 2 years of experience in data science or quantitative analysis. Proficiency in programming languages such as Python, R, or SAS. Experience with machine learning frameworks and tools such as TensorFlow, scikit-learn, or similar. Familiarity with big data technologies such as Hadoop, Spark, or similar is a plus. Strong analytical and problem-solving skills, with the ability to interpret complex data and provide actionable insights. Experience with financial data analysis and visualization tools is a plus. Excellent written and verbal communication skills, with the ability to clearly present complex technical information to non-technical stakeholders. Ability to write clear and comprehensive documentation. Familiarity with regulatory guidelines and standards related to model risk management, such as SR 11-7, and other relevant regulations is a plus. Knowledge of Machine Learning/statistical frameworks, such as Jupyter, AWS, Azure ML, Knime, SAS, Strata, etc. #J-18808-Ljbffr