Robert Seibert
About Robert Seibert
Robert Seibert is a Senior Manager of Data Science at Tapestry, where he leads initiatives in machine learning and data analytics. He has a background in financial analysis and holds degrees in History and Accounting from Providence College.
Current Role at Tapestry
Robert Seibert serves as the Senior Manager of Data Science at Tapestry, a position he has held since 2017. In this role, he leads the data science forecasting initiative, focusing on improving forecasting accuracy across the organization. His expertise in machine learning and data analytics contributes to the development of innovative solutions for inventory and pricing analytics, as well as gross margin analytics and financial planning and analysis (FP&A).
Previous Experience at Tapestry
Prior to his current role, Robert Seibert held multiple positions at Tapestry. He worked as a Financial Analyst in Gross Margin and Inventory Analytics from 2012 to 2014, followed by a year as a Senior Financial Analyst in the same area from 2014 to 2015. He then transitioned to the role of Manager of Gross Margin and Inventory Analytics, where he remained until 2017. His tenure at Tapestry provided him with extensive experience in financial analysis and inventory management.
Experience at YAI
Before joining Tapestry, Robert Seibert worked as a Senior Budget Analyst at YAI from 2010 to 2012. In this position, he was responsible for analyzing budgetary data and supporting financial planning efforts. His experience at YAI contributed to his analytical skills and understanding of financial operations.
Educational Background
Robert Seibert holds a Bachelor of Arts in History from Providence College, where he studied from 2004 to 2008. Additionally, he earned a Bachelor of Science in Accounting and Finance from the Providence College School of Business during the same period. This dual educational background provided him with a strong foundation in both historical analysis and financial principles.
Technical Skills and Projects
Robert Seibert specializes in deploying machine learning solutions, particularly for inventory and pricing analytics. He has experience with AWS technologies, including AWS Lambda, Dynamo DB, Amazon Sagemaker, AWS S3, and AWS EC2. Notably, he developed a product similarity API using PyTorch and a FAISS index, and he is involved in creating in-store product recommendation systems and customer clustering models. He also serves as the team's expert in Deep Learning, utilizing frameworks such as PyTorch and TensorFlow.