Job Duties

• Design, develop, implement, and document advanced quantitative models and scenarios for time-series forecasting of revenues, expenses, and balance sheet items. Incorporate a broad range of economic, financial, and business variables to address practical issues in budget planning and management, and conduct uncertainty quantification.

• Develop and deploy explainable Machine Learning (ML) models for financial event prediction, revenue forecasting, and expense projection. Derive actionable insights to support corporate strategy, budget planning, regulatory compliance, and internal governance reviews.

• Collaborate with cross-functional stakeholders across business divisions, Finance, Risk, and other Core corporate departments. Translate complex user needs into precise model specifications, analytical metrics, interactive dashboards, and comprehensive reports tailored for senior leadership and operational teams.

• Execute the end-to-end model development lifecycle, encompassing data collection, exploratory data analysis, feature engineering, variable selection, model selection, hyperparameter tuning, validation, and scalable deployment on AWS Cloud.

• Design and engineer Artificial Intelligence (AI) agentic systems to deliver analytical, data science, and reporting capabilities through both interactive and batch reporting interfaces. Manage agent orchestration, context management, knowledge base integration, and overall AI lifecycle management.

• Conduct rigorous simulation studies, provide theoretical justifications, and perform model performance testing. Create and maintain comprehensive technical documentation to support Model Risk Management (MRM) reviews, facilitate finding remediation, and ensure ongoing model monitoring.

• Develop, implement, and document scenarios comprised of a broad range of economic and financial variables for budget planning and management within the Firm.

• Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues.

• Analyze large datasets (structured and unstructured) to build predictive models of business-relevant financial variables (revenues, expenses, and balance sheet).

• Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming.

• Build and challenge revenue and expense models, identifying and quantifying vulnerabilities across financial planning and forecasting.

• Create and maintain clear and complete technical documentation of the model performance testing approach and process.