Top 1 Data governance lead Jobs in Detroit Mi

Automotive Warranty Claim Data Lead
Stellantis —Auburn Hills, MI
- Azure
- Computer Science
- 3 years

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Automotive Warranty Claim Data Lead

Stellantis -
Auburn Hills, MI

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Job Details

Qualifications

Managing data teams
Leading team collaboration initiatives
Managing projects
Project leadership
Manufacturing company experience
Stakeholder management

Full Job Description

The Automotive Warranty Claim Data Lead is a critical role responsible for driving data informed decisions across the organization by leading the collection, analysis, and reporting of automotive warranty claim data. This position will lead efforts to identify emerging quality issues, forecast warranty costs, and provide actionable insights to Engineering, Quality, Service, and Finance teams to improve product reliability and minimize warranty expenditure. The Data Lead will also be responsible for the integrity and strategic use of the core warranty database.

Key Responsibilities:

Data Strategy & Analytics Leadership:

Lead the development, implementation, and maintenance of advanced analytical models (e.g., predictive models, time-series forecasting) to project future warranty claims, failure rates, and cost trends.
Spearhead deep-dive analysis on complex warranty datasets to uncover root causes of product failures, identify anomalies, and detect potential over repair
Develop and manage the overall data governance strategy for the warranty claims database, Palantir/MAP, to ensure data accuracy, consistency, and reliability across

all reporting and analytical platforms.

Design, build, and maintain intuitive data dashboards and Key Performance Indicators (KPIs) to monitor group warranty goals, claim trends/broken clean points, and product reliability for all relevant stakeholders.

Reporting & Cross-Functional Collaboration:

Prepare and present regular, comprehensive reports on warranty performance, cost drivers, and key findings to senior management, including executive-level summaries.
Collaborate cross-functionally with Quality, Engineering, Manufacturing, and Service teams to translate data insights into concrete product or process improvements.
Provide actionable intelligence to the Technical Service team, enabling faster resolution of recurring and high-cost failure modes in the field.
Support the Finance and Accounting departments with accurate warranty cost accruals, budget forecasting, and financial reporting based on data-driven projections.

Process & System Improvement:

Identify opportunities to leverage advanced technologies, such as Machine Learning or AI, to enhance data mining, claims validation, and root cause analysis processes.
Act as the subject matter expert and system administrator for key warranty and data platforms (e.g., Global Warranty Management System, BI tools, data warehouses).
Champion continuous process improvement within the warranty data workflow, seeking to automate data pipelines and streamline reporting to improve efficiency.
Ensure all data handling and reporting complies with company policies, legal requirements, and industry standards.

*
Required Qualifications:*

Bachelor's degree in Data Science, Statistics, Engineering, Computer Science, or a related quantitative technical field.
5+ years of experience in data analysis, business intelligence, or data science.
3+ years of experience specifically within the automotive, manufacturing, or heavy-equipment industry, with a focus on warranty data, reliability engineering, or technical services.
Proven experience in a leadership or lead-analyst role, mentoring junior team members or managing complex analytical projects.

Preferred Qualifications:

Master's degree.
Proficiency in SQL for complex data querying, manipulation, and analysis of large datasets.
Programming skills in a statistical or data science language, such as Python or R.
Proficiency with Business Intelligence and data visualization tools (e.g., Tableau, Power BI, QlikSense) to create insightful reports and dashboards.
Familiarity with warranty management systems (e.g., Global Warranty Management) and enterprise data environments (e.g., data lakes, cloud platforms like AWS, Azure,

or GCP).

Solid understanding of statistical methodologies for time-to-failure analysis (e.g.,Weibull), forecasting, and hypothesis testing.
Knowledge of quality improvement methodologies (e.g., Six Sigma, FMEA).
Experience with machine learning frameworks and modeling for predictive maintenance or failure prediction.
Prior experience working directly with automotive dealer management systems (DMS) data.
Analytical and Problem-Solving Skills: Exceptional ability to interpret complex technical and financial data, distill key findings, and propose practical solutions.
Communication: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical analysis to both technical and non-technical

audiences, including executive leadership.

Leadership and Influence: Demonstrated ability to lead projects, drive change across cross-functional teams, and influence key stakeholders without direct reporting authority.
Attention to Detail: Rigorous attention to detail to ensure the accuracy and integrity of all data and analytical reports.

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