At Fannie Mae, I modernized forecasting components into modular, testable microservices, reducing development time by 35%. I also designed data-validation workflows that prevented invalid executions and saved $40,000 per run.
As a Senior Associate, I optimized home-price forecasting workflows with AWS Batch, Step Functions, and Lambda, reducing runtime by 60%. I also introduced failover for critical UIs and APIs, achieving 99.99% uptime.
Earlier at Fannie Mae, I migrated SAS forecasting processes to Python-based AWS workloads and built performance-tracking workflows. On my Seat Optimization Service project, I developed an application for team-based seat allocation using a fairness rotation algorithm.
