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Keurig Dr Pepper

Inventory Operations Analyst

Frisco, TX,US, US; Dallas, TX,US, US

Keurig Dr Pepper is hiring an Inventory Operations Analyst in Frisco, TX,US, US; Dallas, TX,US, US. Posted September 29, 2026.

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Job at a glance

Company
Keurig Dr Pepper
Location
Frisco, TX,US, US · Dallas, TX,US, US
Workplace
On-site
Employment
Full-time
Sector
Operations & Manufacturing
Posted
September 29, 2026
Apply by
March 28, 2027

About the job

Develop and maintain weekly BSO reporting and dashboards to provide visibility across regions, functions, and key drivers Analyze trends in trade break, obsolescence, shrink, and inventory aging to identify risks and opportunities Translate data into actionable insights and prescribed actions for field and leadership teams Track execution of actions and follow up with stakeholders to ensure accountability and results Support development of standardized reporting tools and help move toward a single source of truth Partner with Finance and Analytics teams to validate data accuracy and quantify financial impact Assist in building and maintaining 30-60-90 inventory tracking and obsolescence management reporting Support cross-functional initiatives related to resets, repack, donations, destructions, and trade break reduction Prepare materials for weekly, monthly, and executive reviews Identify process gaps and recommend improvements to enhance reporting, routines, and performance management Bachelor's degree in Business, Supply Chain, Finance, Analytics, or related field 2-5 years of experience in analytics, operations, supply chain, or related role Strong analytical skills with proficiency in Excel, Power BI, or similar tools Ability to translate complex data into clear insights and actionable recommendations Strong attention to detail and data accuracy Effective communication skills with ability to work cross-functionally Ability to manage multiple priorities and meet deadlines in a fast-paced environment Experience with inventory, supply chain, or DSD operations preferred Comfort working with large datasets and identifying trends and root causes