Cartesian product enthusiast

• Built an end-to-end data pipeline to curate production tables that serve as the basis for all AMXL financial reporting/statement generation for the United States and Canada• Developed a Java client to invoke API calls, allowing end-users to trigger SQL profiles within an internal data orchestration platform, enhancing functionality and supporting automated workflows• Partnered with AMXL’s data science team to publish a SKU demand forecasting model that improved forecast accuracy. Generated training data, calculated forecast accuracy via quantile loss, and assisted in publishing a paper presented to Amazon Scholars• Leveraged the Spark SQL API and AWS Elastic Map Reduce to query multi-PB datasets and model the impact of different regionalization scenarios

• Designed and implemented a data pipeline and root-causing framework for AMXL EU, to optimize inventory positioning. This initiative reduced outbound shipping costs and pinpointed inefficiencies in inbound placement decisions.• Developed an advanced model to analyze inventory turnover against demand fluctuations. This model guided strategic decisions in sourcing, channel selection and forecast accuracy, aligning inventory management with business dynamics.• Engineered a data pipeline and, subsequently, a linear programming model to solve a system of inequalities to derive optimal transfer suggestions for maximizing SKU diversification and minimizing outbound shipment distance for new Fulfillment Centers.• Maintained Redshift cluster and leveraged Spark on EMR for large-scale data processing. Implemented CI/CD processes for efficient data extraction and transformation. Specialized in query optimization across Spark, Redshift (Fork of Postrgres), and MySQL.

• Redshift cluster management, ensured critical report delivery, query optimization and led SQL training, and created data pipelines using AWS CDK to provision S3, Lambda, SES• Leveraged data science insights to optimize inventory positioning, creating Tableau/Quicksight reports to analyze inventory health and proximity to customer demand; automated inventory selection for new warehouses• Created reporting on demand forecasting, inventory placement, and daily warehouse execution; updated production tables critical to AMXL and vended to partner organizations

• Implemented a depth first search algorithm in a directed graph using Python to optimize connectivity for several programs. The initiative improved network connectivity by 9.43%, reduced touches, and identified duplicate connections• Created queries, data visualizations, and automation projects to improve the workflow of the team and partner stakeholders, saving an estimated 750 hours of work annually• Helped launch new AMXL sites and lead deployment of changes to the direct fulfillment network

• Utilized STATA to aggregate and analyze fisheries datasets and provided further insights using Tableau and Excel• Automated data cleaning tasks with Python scripts• Contributed to a paper that implemented dynamic socio-environmental network models to provide insights into wild-caught fishery populations • Read and synthesized resource economics academic literature