Case Portfolio

Deployed large-scale Azure ML-powered sales forecasting frameworks using Python and PyTorch via Azure Databricks and Azure Synapse clusters. Built and maintained executive-grade Power BI dashboards reporting directly against live enterprise data sources across Microsoft's internal infrastructure. Delivered production machine learning model deployments within Microsoft's security and compliance constraints.

Engineered compliant regulatory ETL pipelines and predictive modeling arrays monitoring high-volume wire transfer operations inside a federal banking compliance environment. Performed mass data extraction and manipulation over large relational datasets using SAS and MySQL. Developed predictive models and statistical analysis frameworks for external clients and internal business performance reporting. Assisted in the development and maintenance of regulatory reporting systems aligned to federal banking standards.

End-to-end data science project lifecycle from use case framing through model deployment and operational feedback loops. Applied regression, classification, and clustering models with prescriptive analytics embedded directly into live operational business lines. Systems operated continuously against production data with documented downstream revenue and efficiency impacts.

Led a team of 15 researchers, translating business requirements into structured analytical deliverables. Developed process models, prepared statistical reports, and managed cross-team workflow delegation. Promoted to lead role within the project and immediately drove measurable performance improvements across the department within the first three weeks.

Processed specialized trust functions — wire transfers, cash and trade processing, daily cash reconciliation, ACH, debt service payments, and compliance monitoring across 35+ accounts for 5 Fortune 500 companies, executing 50+ daily transactions consistently totaling over $20 million. Built machine learning models to identify fraudulent transactions from mobile and money send channels based on unsupervised transaction histories from large data lakes.

Specialized in data collection, storage, and analysis for AHN's Server Team. Built SSIS packages for ETL to data warehouse. Designed and populated tables and databases for metric reporting. Developed NLP sentiment analysis model to prioritize support tickets past resolution time. Automated reporting pipeline using R, VBA, and Tableau. Integrated into Hadoop Data Lake with the data engineering team.

SMB data analytics consulting, A/B testing protocols, automated client segmentation systems, data-driven SEO optimization frameworks, and custom AI report generation pipelines. Delivered measurable growth in web traffic and engagement across multiple independent client engagements.