Descripción

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 strengthen system visibility, reduce manual work, and improve total cost and execution performance across GPSC Logistics & Packaging.

What You’ll Do

Build and maintain Power BI dashboards, recurring reports, and self-service analytics for logistics, containers, packaging, and related cost or flow performance metrics.
Combine and validate data from multiple systems and sources to create a reliable view of packaging plans, container activity, inbound logistics performance, and cost opportunities.
Analyze packaging and logistics data to identify trends, root causes, risks, and improvement opportunities tied to cost, density, freight, launch readiness, and plant execution.
Support business decisions by translating data into clear recommendations for managers, buyers, packaging teams, logistics partners, and plant stakeholders.
Develop reporting and analyses tied to approved packaging plans, PFEP visibility, sourcing alignment, and inbound execution outcomes.
Help improve data quality and process discipline by identifying gaps, validating assumptions, and reducing manual interpretation of supplier and packaging inputs.
Use tools and data related to OLCT, PFEP, GM 1738 requirements, and other packaging or logistics reference sources to support analysis and reporting.
Partner cross-functionally with GPSC Purchasing, PFEP Packaging & Data Management, packaging engineers, container teams, logistics teams, and plants to align data with operational needs.
Support special projects involving container flow, expendable packaging, claims, system visibility, KPI development, and total enterprise cost analysis.
Drive continuous improvement by automating reporting, simplifying analysis workflows, and enabling faster, data-driven decision making across the organization.

Your Skills & Abilities (Required Qualifications)

5+ years of experience in data analytics, business intelligence, data science, machine learning, supply chain analytics, packaging analytics, logistics analytics, or a similar role. (any internship or co-op experience will not be considered)
Strong SQL proficiency and the ability to work across large, complex, and sometimes imperfect datasets.
Python proficiency, including experience with libraries and tools used for data analysis and automation.
Experience with Power BI or similar visualization tools, including dashboard design and KPI reporting.
Experience with Databricks, Spark, and/or other cloud-based data platforms for large-scale data processing.
Experience designing and implementing ETL/ELT pipelines that integrate data from multiple transactional and analytical systems.
Strong skills in exploratory data analysis to assess data quality, structure, and relationships.
Ability to translate ambiguous business questions into analytical and data problems with clear hypotheses, success criteria, and structured recommendations for technical and non-technical stakeholders.
Ability to lead large-scale development projects with third-party software and analytics companies, including scoping, coordination, and project management.
Strong analytical, problem-solving, and communication skills, with the ability to manage multiple assignments with a high level of autonomy and accountability.

What Will Give You a Competitive Edge (Preferred Qualifications)

Bachelor’s degree in computer science, engineering, statistics, mathematics, physics, supply chain, information systems, or another related quantitative field; advanced degree preferred.
Experience working with packaging, containers, inbound logistics, supply chain operations, automotive, manufacturing, or engineering data.
Familiarity with packaging concepts such as returnable, expendable, primary, back-up, bulk, and unitized packaging.
Working knowledge of OLCT, PFEP, GM 1738, or other GM packaging and logistics systems or standards.
Experience supporting sourcing, should-cost visibility, cost reduction, or operational improvement initiatives.
Experience with descriptive or predictive modeling methods such as regression, clustering, segmentation, random forests, or gradient boosting, applied pragmatically to business problems.
Exposure to advanced ML/AI techniques is a plus, but the primary focus of this role is strong data analytics, EDA, data engineering, and practical business insight generation.

What Success Looks Like

Improved visibility to packaging and logistics performance through trusted dashboards and reporting.
Faster identification of cost, flow, density, and execution issues affecting suppliers, plants, and internal teams.
Better alignment between packaging plans, sourcing decisions, and inbound execution through stronger data discipline and analytics support.
Reduced manual effort and more scalable reporting across logistics and packaging workstreams.

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.)

Este puesto se clasifica como híbrido. Esto significa que se espera que el candidato seleccionado se presente en una ubicación específica al menos 3 veces por semana {o con otra frecuencia indicada por su líder}.

El candidato seleccionado deberá viajar menos del 25 % del tiempo para este puesto.

Este puesto podría ser elegible para beneficios de relocalización.

Información sobre diversidad

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Declaración de igualdad de oportunidades en el empleo (EE.UU.)

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Adecuaciones (EE.UU. y Canadá)

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