Role Summary

Data Intelligence, Supply Chain Planning is part of Supply Chain Intelligence within Global Supply Chain division at Pfizer. We are the PGS Business Owner for all relevant supply chain planning data that is required to ensure the Kinaxis Rapid Response planning platform performs as designed and provides PGS with 5-year, resource constrained network supply plans that are utilized by the global stakeholder community for all relevant business processes.

The Next Generation Planning & Detailed Scheduling software platform (nGPS), data & related business process is intended to plan & schedule Pfizer’s entire internal & external supply network from procurement of key raw materials through in-market replenishment of finished goods to the 1st paying customer. This platform will support ~$60B+ annual revenue, $15B+ annual COGS & $15B+ inventory investment.

This new role is one of two new roles, each of which will have segmented responsibility for critical data domains: valid transportation lanes with mode & duration; safety stock polices including time varying strategies; lot size & replenishment frequency strategies; shelf life planning parameters; goods issue & goods receipt lead-time planning parameters; bottle-neck resource capacity availability planning parameters; supplemental planning bill-of-material information including planning success rates; planning horizon recipe information including capacity consumption; responsible planner master data.

Role Responsibilities

This exciting new role have responsibility to maintain and continuously improve current core data domains with expectation of future expansion to adjacent data domains. Core responsibilities of these roles include the following:

• Perform wide range of tasks relating to data extraction, data scrubbing, and data transformation, maintenance of metadata and data dictionaries.

• Design and implement governance and life cycle management processes for all in scope data domains.

• Run SQL queries to identify data issues, data fixes, manual extracts, etc.

• Design conceptual, logical, and physical Data modelling to support the Enterprise Master Data Management system.

• Apply data quality techniques such as match, merge, profiling, score carding, data standardization, and parsing using enterprise data quality platforms.

• Assist with troubleshooting and resolving MDM-related issues and provide proven solutions aligning with master data strategies and best practices.

• Perform data fixes using advanced SQL and staging tables.

• Provides subject matter expertise on: Master Data Management and ETL (Extract, Transform & Load) Concepts.

• Ultimate accountability for the accuracy of all in-scope data including execution of any data updates required to ensure accuracy when endorsed by leadership.

• Collaboration & partnership with all relevant connected business processes & Digital solutions to ensure both alignment of strategic roadmaps and seamless integration of existing data domains & business processes. This includes establishing and maintaining key partnerships within the OpU’s, Quality, S&OE, GSC & Digital as well as key partnerships with the solution/program owners of SAP ERP, Planet Together, PGS UDH, Predictive Plant Acceleration, Manufacturing Cycle Time, Network Strategy systems, PGS Analytics, etc.

• Development, documentation and communication to the stakeholder community of the intended purpose of all in-scope data attributes and their associated definitions and acceptable values ensuring organization alignment & understanding.

• Design, development, implementation & operation of all required business processes to ensure data health monitoring, rapid data health issue identification & remediation. Visual management of performance metrics at all relevant management levels including the leverage of next generation AI technologies to drive data health performance.

• Execute any required stakeholder training & participate in any required data governance forums.

Qualifications

• Bachelor’s degree in Mathematics, Statistics, Computer Science, Management Information Systems or closely related technical discipline; master’s degree in Data Science is a plus.

• 7+ years proven experience in data management / data architecture.

• Proficiency in SQL & Python or R.

• Extensive experience with ETL processes and tools.