• Delivered 20+ projects to multiple concurrent clients, including a pivotal role in a million-dollar contract, by leading client-facing data operations and fostering cross-team collaboration• Enhanced data operations efficiency and team coding practices by developing templates for data cleaning and sales data analysis, improving project delivery speed and reducing troubleshooting time• Developed a Python script to call and debug an in-house API in order to prepare the results for a client-facing deliverable• Authored training pages for onboarding which increased cross-team knowledge sharing and process improvements beyond my immediate team• Increased client satisfaction and operational efficiency by streamlining client communications and improving accuracy in data management and data lineage, leading to more effective project outcomes and faster problem resolution• Aggregated client observations and requests into technical documentation for the Product team to integrate in future iterations of the tool

- Led a cross-functional team to improve supply chain analyses that identify items for improvement to recommend logistics-focused solutions to varying levels of leadership - Led a development team to program a graphical user interface and improvements to previously developed recommendation system, increasing speed by 12x over previous algorithm- Designed/programmed an analytic pipeline transforming database output files to presentation worthy analyses for ship data that reduced manual work by 192 hours/month- Assumed leadership responsibilities in coordinating team tasking, realigning expectations with stakeholders, and presenting project results that resulted in recognition from management- Programmed a recommendation system using natural language processing (NLP) to identify similar failure records that reduced human analytic time from 4 years to 9 minutes- Refined procedures and coordinated team tasking based on various stakeholder requirements that resulted in improved data reports and realignment of thousands of dollars of funding to necessary problem areas- Developed data processing tools with machine learning and natural language processing Python packages to aid analysts in data cleaning and investigating trends in text heavy datasets that reduced processing times for analyses by ~40%- Designed and programmed improvements into processes related to equipment failure analyses with R and VBA that reduced the need for manual efforts equating to 1.2 full time employees