• Modelled and conveyed to leadership E2E costs on whether Global Mile (GM) should ship containers to Inland Ports or use existing business models.• Automated Month End Close (MEC) and Weekly Business Review (WBR) with a QuickSight Report that show controllable and uncontrollable factors to automate commentary.• Presented FBA Strategic Planning Session model to leadership on cost scenarios to optimize GM sortable freight• Developed a dwell day sensitivity model to identify breakeven points between GM transloader and container direct businesses. • Owned WBR, MEC, and planning cycles consolidation and commentary for NA Destination in partnership with accounting and FP&A.• Modelled the E2E cost benefit of sending units in a 40ft vs 20ft container.

• Owned 3P container yards for Amazon Global Mile business including month end close and planning cycles• Created capacity and cost forecasting methodologies on yard spots needs over the next 3 years. Constructed a non-linear dwell day model using Python that takes into account seasonality trends as a main input• Created and documented a Global Mile Yards One Stop Shop for reporting as a solution for scalability, trusted financials, deep dive data, and sustainability. The dashboard shows utilized spots across NA, cost, planning cycles, and incorporated dynamic forecasting models for sensitivity analyses • Saved $1MM by reducing fixed capacity based in ports below forecasting models• Launched new yards in ports with contracts that minimize underutilization costs, saving over $1.3MM• Constructed financial analysis to switch from 3P to 1P in high capacity ports, saving over $1.5MM in the 1st year• Increased yard manifest accuracy by 12% with new reporting

• Sole finance partner for an Amazon Robotics (AR) site sending over 4MM units and over 3.3k headcount • Launched and created Operations Finance Technical Training (OFTT) lunch and learn series to teach financial analysts and managers technical skills including SQL, dashboard making, web scraping, VBA, and ETL jobs.• Programmed and standardized the AR morning report with VP visibility with VBA. The automated report is sent daily to three super regions and the source of truth for rate data. Partnered with data engineers on minimizing timeouts and job failures. • Created a multi-network QuickSight dashboard that deep dives function level associate hours and has reduced overstaffing• Created an AR fully burdened wage rates by shift code to identify the difference in day vs night shifts, full-time vs part-time, rate adjusted cost per hour, and historic attrition impact on hourly cost.

• Lead and modeled labor plans for a fulfilment center that ships over 200k weekly units and had over 750 headcount• Developed an ETL pipeline and regional report utilizing ETL Manager, VBA, and SQL to report regional hours lost from associates not showing at beginning of the shift. The automated report reduced CMH2 outbound hours by 29% YoY and scaled across the entire traditional non-sort network (TNS).• Constructed a QuickSight Dashboard for Non-Standard Hours tracking that reduced hours by 60% across my region and ranking #1 across all TNS regions• Constructed a QuickSight Dashboard for FLEX Hours tracking that gave Area Managers visibility on accepted shifts and if associates are tracking towards expected hours quota. • Developed a regression model utilizing Python across the entire Amazon Robotic network on 12 major KPIs to identify material trends and areas for operations to focus time on

• Measured data accuracy of several forecasting methodologies using Excel, VBA, and SQL to predict total inbound carton rate. Utilized time series error minimization methods and hypothesis testing to reduce forecasted hours delta and deviation within all Inbound Cross Docks• Created a data pipeline for my new forecasting methodology utilizing historical data and volume outlier removal which reduced errors and deviations by 20+% compared to the current methodology• Implemented superior forecasting methodology into a QuickSight dashboard for stakeholders to start minimizing voluntary time off and overtime decisions for IXDs. Up to $27,700 weekly savings• Lead and deep-dived a group case study on bridging the capacity gap for the 2020 holiday season

• Rapid Change Over – Created a VBA Userform that eliminated touches, used conditional procedure lists, and predicted change over time. Updated database for centerlines and utilized time studies and travel diagrams. Decreased times by 20% on 2 lines. $158,000 annual savings • Increasing Line Speed – Planned and lead two validations to increase line speed by 60%• Driver Reduction – Transitioned module to a new line supply process that decreased drivers and idle time. Conducted time studies and diagnosed key gaps. $282,000 annual savings • Scrap Reduction – Designed, planned, and lead 7 Fast Cycle Learning Tests to estimate contractor numbers. Reduced 138,000 kgs of corrugate. $146,000 annual savings

• Met with clients to communicate the city’s needs on new and existing projects • Worked with Professional Engineers on client specifications and accounting • Conducted field work to gather data points on wastewater lift stations • Analyzed flow and pressure data in Excel to discover sources of outliers

• Met with constituent representatives to manage federal casework• Conducted research on policy issues for the Congressman to draft legislation• Scheduled tours for constituents to visit government buildings• Managed an Excel workbook for the Congressman’s campaign network for re-election