General Description
Popular is seeking a quantitative analyst who will conduct the validation for quantitative risk models and core system applications subject to Model Risk and Governance policy requirements such as credit risk, operational risk, scenario variables/macroeconomic forecasting models, Bank Secrecy Act (BSA) / Anti Money Laundering (AML) and fraud system rules, etc. which are used to assess the adequacy of risk modeling for regulatory and business requirements.
Essential Duties and Responsibilities
- Validates, tests, documents, implements, and/or oversees usage of advanced quantitative/statistical and AML models. The statistical models are utilized in forecasting of the bank's deposit, revenues, stress scenario and allowances. BSA/AML models cover Anti Money Laundering, Sanction Screening, Know Your Customer (KYC), Client Due Diligence (CDD), and other forms of financial crimes monitoring;
- Perform independent challenges of machine learning models used for fraud detection and fraud risk management. Fraud models cover transaction authentication (debit cards, credit cards, ACH) and account originations (loans, credit cards and deposit accounts);
- Deliverables include the creation of validation documentation such as: presentations, written reports, model or reporting code documentation, business requirements, monitoring reports and related code, and procedures;
- Provide effective challenge on the conceptual and technical soundness of the models' design, theory, and framework through various testing following guidelines based on SR 11:7;
- Interact with stakeholders such as model developers, model sponsors, model users, and production, for model risk management related activities;
- 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;
- Working optimally as a team member with other quantitative analysts at Popular, as well with external consultants;
- Evaluating model performance monitoring process, and conducting model annual reviews; and
- Keep up to date with regulatory and legal requirements.
- Communicate clearly the results of analysis and potential outcomes of model validations to key decisions makers.
Education
- Bachelor's/Master's Degree in Computer Science, Mathematics, Applied Statistics, Data Science Physics or in a quantitative field.
Experience
- At least 2 years of experience in model implementation/validation/development, experience in machine learning is desirable. Master's degree in the abovementioned fields is a plus.
- Knowledge in BSA/AML/OFAC/Fraud systems or regulations is a plus.
Other Qualifications
- Strong statistical modeling and machine learning background based on technical training or advanced education in a quantitative field. Understanding of and experience with machine learning methods, including classification theory, tree:based modeling methods (Random Forest, GBT, XGBoost), neural networks, logistic regression, and others.
- Excellent problem solving and decision:making skills.
- Ability to work with multiple tasks simultaneously, establish priorities, and meet deadlines.
- Ability to work under pressure and with minimum supervision.
- Strong interpersonal and collaboration skills
- Negotiation skills
- Excellent written and verbal communication skills in English and Spanish
- Computer and technological skills: Proficiency in the Microsoft suite of products (i.e., PowerPoint, Excel, Power BI, Word, Azure, etc.)
- Strong experience using Python, R, or other programming languages to manipulate data, visualize and draw insights from large data sets.
- Knowledge of Machine Learning/statistical frameworks, such as Jupyter, AWS, Azure ML, Knime, SAS, Strata, etc.
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