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Overview

General Motors is looking to hire a Data Analyst to contribute to the GPSC Logistics & Packaging team. Positioned within the business sector of logistics and containerization, this role influences packaging strategy, supplier collaboration, inbound logistics flow, and system transparency covering Original Equipment (OE) and Customer-Carried Assets (CCA).

Key Responsibilities

Create a dependable data infrastructure by integrating, cleansing, and standardizing information from various enterprise systems, packaging databases, and operational reports.
Develop scalable dashboards and reporting tools that provide leadership and stakeholders with insights on packaging plans, container operations, inbound logistics efficiency, cost factors, and key performance indicators.
Establish analytical workflows to find opportunities for cost savings, enhanced pack density, disruption detection, claims analysis, and process improvements along the logistics and packaging value chain.
Conduct exploratory data analysis and root cause reviews to improve data quality, clarify operational challenges, and deliver actionable insights for sourcing, packaging, and logistics stakeholders.
Design and maintain ETL/ELT pipelines and curated datasets to simplify data access for ongoing reporting, self-service analysis, and advanced future modeling.
Collaborate cross-functionally with Purchasing, PFEP, packaging engineers, container teams, logistics operations, IT, finance, and plant personnel to ensure alignment on business queries, data sourcing, and prioritization of analytics projects.
Translate vague operational questions into specific analytics projects with defined hypotheses, success metrics, deadlines, and practical business recommendations.
Advance future analytics capabilities including segmentation, forecasting, and predictive analytics to enhance decision-making without complicating core reporting and insights.
Promote process discipline and thorough documentation of key data definitions, assumptions, source logics, and reporting standards to ensure reliability and reproducibility.
Present a strategic roadmap outlining short-, medium-, and long-term analytics enhancements aimed at boosting system visibility, minimizing manual tasks, and elevating total cost and performance across GPSC Logistics & Packaging.

Additional Duties

Create and maintain Power BI dashboards, recurring reports, and self-service analytics associated with logistics, container activity, packaging, costs, and flow metrics.
Integrate and verify data from multiple sources to form a trusted picture of packaging plans, container movements, inbound logistics, and cost-saving opportunities.
Analyze logistics and packaging data to detect trends, identify root causes, assess risks, and highlight improvement possibilities related to costs, density, freight, launch readiness, and plant execution.
Support informed decision-making by converting complex data into clear, actionable suggestions for managers, buyers, packaging teams, logistics partners, and plant teams.
Develop reports aligned with approved packaging plans, PFEP visibility, supplier sourcing, and inbound execution outcomes.
Enhance data integrity and process adherence by identifying discrepancies, validating assumptions, and reducing manual interpretations associated with supplier and packaging data.
Utilize tools and data relevant to OLCT, PFEP, GM 1738 standards, and other logistics and packaging references to underpin analysis and reporting.
Coordinate efforts with various departments such as Purchasing, PFEP Data Management, packaging engineers, container and logistics teams, and plant units to ensure data alignment with operational requirements.
Engage in special projects focused on container flow, expendable packaging, claims management, visibility systems, KPI creation, and comprehensive enterprise cost evaluation.
Drive ongoing improvements by automating reports, streamlining analysis processes, and enabling faster, data-informed decision-making throughout the organization.

Mandatory Qualifications

Minimum of five years’ professional experience in data analytics, business intelligence, data science, machine learning, supply chain, packaging or logistics analytics. Internship or co-op experience is excluded.
Advanced proficiency in SQL, capable of handling large, complex, imperfect datasets.
Proficient in Python programming and its data analysis and automation libraries.
Experience with Power BI or similar visualization tools, including dashboard construction and KPI management.
Familiarity with Databricks, Spark, or other cloud-based processing platforms for large data sets.
Skill in designing and managing ETL/ELT pipelines connecting diverse transactional and analytical data systems.
Strong exploratory data analysis capabilities to evaluate data quality and relationships.
Ability to interpret unclear business questions into structured analytical challenges with defined hypotheses, metrics, and recommendations for technical and non-technical audiences.
Proven leadership in large-scale projects involving third-party software and analytics partners, managing scope, coordination, and project delivery.
Excellent analytical thinking, problem-solving aptitude, and communication skills, with demonstrated autonomy and responsibility in managing multiple tasks.

Preferred Qualifications

Bachelor’s degree in computer science, statistics, mathematics, engineering, supply chain, information systems, or a related quantitative discipline; advanced degrees preferred.
Experience with data from packaging, containers, inbound logistics, supply chain, automotive, manufacturing, or engineering contexts.
Understanding of packaging types and concepts such as returnable, expendable, primary, back-up, bulk, and unitized packaging.
Knowledge of GM-specific logistics and packaging systems including OLCT, PFEP, and GM 1738 standards.
Background in supporting sourcing, should-cost analysis, cost reduction, or operational enhancements.
Experience applying descriptive and predictive modeling methods like regression, clustering, segmentation, and ensemble methods to business problems.
Exposure to advanced machine learning or AI techniques is advantageous though the focus remains on data analytics, exploratory analysis, data engineering, and practical insight generation.

Success Indicators

Enhanced transparency in packaging and logistics performance via dependable dashboards and reports.
Accelerated recognition of cost, flow, density, and operational issues impacting suppliers, plants, and internal teams.
Improved alignment between packaging plans, sourcing efforts, and inbound operational execution through stronger data discipline and analytical support.
Decreased manual efforts and enhanced scalability in reporting processes across logistics and packaging domains.

Additional Information

GM does not offer immigration sponsorship for this position. Candidates requiring sponsorship now or in the future should not apply. The role is primarily hybrid; the selected candidate is expected to work onsite at least three days per week or as directed by management, with travel less than 25%. Relocation benefits may be applicable.

About General Motors

General Motors envisions a future with zero traffic accidents, emissions, and congestion. The company is dedicated to driving transformative changes that improve safety, environmental impact, and fairness.

Why Join General Motors

GM promotes a workplace where diversity, inclusion, and belonging thrive. Every individual is encouraged to contribute towards meaningful change in culture and operations.

Benefits Overview

From the very first day, GM offers resources supporting employee well-being both at work and at home, helping individuals achieve their ambitions within a rewarding career framework.

Equal Employment Opportunity and Accommodations

GM is committed to non-discrimination and fosters an inclusive environment where employees grow and develop. All hiring decisions are made without bias relating to protected statuses under law. Reasonable accommodations are provided for individuals with disabilities during the application process upon request, through specified contact channels.

Data Analyst

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