Quantitative Analyst

Job Type

General Description

Develops, interprets, and implements statistical, financial, and accounting concepts and quantitative techniques to support credit risk modeling, financial forecasting, model monitoring, and reporting. Performs complex data analysis and develops reproducible analytical processes using statistical programming tools. Identifies opportunities to responsibly apply automation, generative artificial intelligence, and AI-enabled workflows to improve efficiency, consistency, and documentation of quantitative related processes.

Essential Duties and Responsibilities

• Apply statistical theory, econometric methods, and quantitative techniques to collect, organize, validate, interpret, and summarize numerical and text data to provide usable information. Interpret quantitative data and design statistical models for researching problems or questions.
• Perform data analysis using statistical programming software such as Python, R, or SAS, with emphasis on accuracy, reproducibility, data quality, and effective analytical controls.
• Analyze and interpret data to identify trends, patterns, anomalies, and emerging risks that may affect performance, model results, or estimates.
• Develop and maintain analytical programs, reusable scripts, data validation routines, and automated workflows that improve the efficiency and consistency of quantitative processes.
• Perform independent review, reproduction, and validation of analytical results, including reconciliation of data, identification of discrepancies, and documentation of findings and resolutions.
• Gather, evaluate and relay both internal and economic data to predict different types of risks by making use of econometric and statistical methods.
• Prepare clear technical documentation, analytical reports, presentations, and supporting materials that support the explanation of methodologies, assumptions, results, limitations, and key business implications.
• Identify and evaluate appropriate opportunities to use generative AI, AI agents, or related technologies to support research, documentation, data analysis, workflow automation, and process efficiency, subject to applicable governance, data privacy, information security, validation, and human-review requirements.

Education

Bachelor's degree in Statistics, Mathematics, Mathematics, or Economics with a strong quantitative concentration, Data Science, or another closely related quantitative discipline.

Master's degree in Statistics, Mathematics, Economics, or Data Science

Experience

Bachelor's degree in a quantitative discipline with at least three years of relevant experience; or Master's degree in a quantitative discipline with at least two years of relevant experience.

Relevant experience should include a combination of the following:

• Statistical analysis, econometric modeling, predictive modeling, financial modeling, credit risk analysis, or model monitoring.
• Experience analyzing large or complex datasets and translating quantitative findings into conclusions and recommendations for management.
• Practical experience using Python, R, SAS, or comparable analytical tools to perform data preparation, statistical analysis, modeling, validation, or process automation.
• Exposure to generative AI, large language models, AI agents, prompt design, retrieval-augmented generation, or AI-enabled workflow automation is preferred but not required.

Other Qualifications

• Strong analytical and critical-thinking capabilities, with the ability to assess assumptions, evaluate data quality, interpret model results, and identify limitations or emerging risks.
• Solid understanding of statistical concepts and methods, including regression, classification, time-series analysis, hypothesis testing, sampling, model performance evaluation, and data visualization.
• Knowledge of accounting and financial concepts relevant to credit loss estimation, including the current expected credit losses methodology, portfolio performance, financial reporting, and internal controls.
• Proficiency in at least one statistical programming language, preferably Python, R, or SAS. Programming capability is expected to support quantitative analysis, reproducibility, and automation.
• Familiarity with version control, structured code documentation, testing, or peer-review practices is desirable.
• Knowledge of generative AI and agent-based technologies, including potential applications, limitations, responsible-use considerations, privacy, security, and human oversight, is a plus.
• Ability to communicate complex quantitative and accounting concepts clearly to technical and nontechnical audiences.
• Advanced PC skills, including Microsoft Excel, PowerPoint, Word, SharePoint, and related Microsoft 365 applications.
• Fully bilingual in English and Spanish.

Region Locations

Popular Center 011

San Juan, Puerto Rico

Values

Additional Requirements