Quick overview
Job description
Job Title: Digital Transformation & Change Management Analyst
Reedsville PA 17084
12 Months
Job Responsibilities
Data Strategy, Architecture & Analytics
• Leads data template strategy from design through implementation, serving as a trusted advisor to Supply Chain Teams and ensuring alignment with organizational objectives.
• Designs and constructs complex master data structures with a deep understanding of analytics needs, enhancing efficiency and optimizing data utilization to facilitate more insightful analysis and decision-making.
• Translates intricate business requirements into scalable technical solutions that meet data warehousing design standards, enabling effective data management and analysis under minimal supervision.
• Defines concepts and scenarios to guide the development of reporting and analytics solutions, ensuring clarity and alignment with business objectives.
• Utilizes statistical methods and data visualization techniques to address high-complexity requests, extracting actionable insights from data to inform decision-making processes.
• Designs and documents technical solutions to complex requirements using various tools, including HADOOP, Informatica, R, Python, and Tableau.
• Maintains comprehensive documentation of master data processes, procedures, and guidelines to facilitate knowledge sharing and ensure continuity of operations.
Process Improvement & Technical Delivery
• Leads process improvement projects to enhance the efficiency and effectiveness of reporting and analytics processes, driving continuous improvement and innovation.
• Applies a global approach to analytical solutions, both within a business area and across the organization, fostering collaboration and knowledge sharing.
• Leads technical data delivery for projects, coordinating the implementation of data solutions and ensuring successful project outcomes.
Change Management, Training & Implementation
• Supports change management activities related to digital tool implementations by developing and executing communication plans to ensure alignment across operators, supervisors, and managers.
• Develops, maintains, and delivers role-based training materials and sessions to support the adoption of new digital tools, processes, and workflows within manufacturing operations.
• Coordinates and tracks training completion, supporting go-live readiness and ensuring stakeholders are equipped to effectively utilize new systems and processes.
Requirements
Minimum Required Education
• Bachelor's or Master's Degree in Data Science, Business Analytics, Supply Chain, Information Systems, Operations Research, Manufacturing/Industrial Engineering, or equivalent.
Minimum Required Experience
• Minimum of 2 years of experience with a Bachelor's degree in areas such as Data Analysis, Supply Chain Management, Operations, or equivalent; OR no prior experience required with a Master's degree.
Preferred Qualifications & Skills
Preferred Experience
• Experience in Lean Manufacturing, Supply Chain Engineering, and working in the implementation of Digital Tools within production environments.
Preferred Skills
• Supply Chain Management (SCM)
• Data Analysis & Interpretation
• Data Architecture
• Process Improvements
• Statistical Methods
• Business Acumen
• Business Intelligence Tools
• Database Management Systems (DBMS)
• Structured Query Language (SQL)
• Data Warehousing (DW)
• Regulatory Requirements
• Training Delivery
• Enterprise Resource Planning (ERP) Systems
• Manufacturing Execution Systems (MES)
• Continuous Improvement
Position Details
Justification
Contingent worker needed to support change management and training for the Digital Tools workstream within Project Rocky. This workstream delivers digital SICB capabilities that provide clear, real-time signals to supervisors and operators regarding transducer component builds, along with proactive notifications to management on missed intervals and production risks. This role is critical to mitigating risks to adoption readiness and ensuring targeted Q4 2026 go-live outcomes, which include reduced manual reporting, decreased inefficient labor usage, and improved visibility across up to 80% of production work cells.
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