Jack FitzGerald

Jack FitzGerald is currently employed in an unknown role at Whatnot. Prior to this, they worked as a Global Export Logistics Operations - Transportation Analyst at Amazon from June 2020 to February 2022. In this role, they consolidated data from six years of financial statements in order to conduct a cost-benefit analysis of the impacts on service levels and logistics costs from transitioning to a centralized supply chain and purchasing model. Jack also identified and analyzed cost savings opportunities for three product categories that did not fit the centralized purchasing plan and proposed alternate purchasing SOPs to minimize transportation costs while maintaining optimal service level and decreasing lead time.

prior to their work at Amazon, FitzGerald was employed as a Supply Chain Intern at SRS Distribution Inc. from June 2019 to August 2019. In this role, they were responsible for executing the acquisition playbook and working with acquired employees to identify, count, and re-label thousands of SKUs to ensure inventory levels and merge IT systems.

Before their work at SRS Distribution Inc., FitzGerald was employed as a Special Project Operations Team - Specialist at Uber from May 2018 to June 2020. In this role, they were responsible for identifying and analyzing cost savings opportunities for three product categories that did not fit the centralized purchasing plan and proposing alternate purchasing SOPs to minimize transportation costs while maintaining optimal service level and decreasing lead time.

FitzGerald's first job was as a carpenter at J.M. FitzGerald and Sons from May 2016 to August 2017. In this role, they were responsible for helping to build houses.

Jack FitzGerald has a Bachelor's degree in Logistics, Materials, and Supply Chain Management from the W.P. Carey School of Business. Jack is also certified by LinkedIn in Business Etiquette: Phone, Email, and Text, Creating Your Personal Brand, Giving and Receiving Feedback, Handling Workplace Change as an Employee, and Unconscious Bias.