Job Description
The Role
General Motors is seeking a Data Analyst to support the GPSC Logistics & Packaging organization. This role sits in the business side of logistics and containerization, where the team drives packaging strategy, supplier alignment, inbound flow, and system visibility across OE and CCA.
The Data Analyst will turn complex operational and packaging data into actionable insights that improve cost, pack density, trailer utilization, sourcing visibility, packaging cost capture, and overall inbound execution. This position is ideal for someone who can build dashboards, write code, bring together large datasets, and help business partners make better decisions using trusted data.
Project Scope for the Role
- Establish a reliable data foundation for logistics and packaging by connecting, cleaning, and standardizing data from key enterprise systems, packaging sources, and operational reporting tools.
- Build scalable reporting and dashboard solutions that give leadership and business partners visibility to packaging plans, container activity, inbound logistics performance, cost drivers, and KPI trends.
- Develop analysis workflows that identify cost reduction opportunities, pack density improvements, flow disruptions, claims drivers, and process inefficiencies across the logistics and packaging value stream.
- Support exploratory data analysis and structured root-cause problem solving to improve data quality, clarify business issues, and uncover actionable insights for sourcing, packaging, and logistics teams.
- Design and support ETL/ELT pipelines and curated datasets that make logistics and packaging data easier to use for recurring reporting, self-service analytics, and future advanced modeling.
- Partner cross-functionally with Purchasing, PFEP, packaging engineers, container teams, logistics operations, IT, finance, and plant stakeholders to align business questions, source data, and prioritize analytics work.
- Translate ambiguous operational questions into clearly scoped analytics projects with defined hypotheses, measures of success, timelines, and business recommendations.
- Enable future-state analytics capabilities, including segmentation, forecasting, and predictive analysis, where they can improve decision making without overcomplicating the core reporting and insight needs of the organization.
- Drive process discipline and documentation for key data definitions, assumptions, source logic, and reporting standards so outputs are trusted and repeatable.
- Deliver a roadmap of short-, medium-, and longer-term analytics improvements that strengt