Current Product Reliability Engineer

The Current Product Reliability Engineer applies engineering and statistical analysis principles to evaluate and improve the reliability of existing products and components across the Cummins product portfolio. This role supports reliability analytics activities by analyzing warranty and failure data, identifying trends, assessing the impact of design changes, and supporting corrective and preventive actions to improve product performance and customer satisfaction. The individual collaborates with cross-functional teams including Quality, Service, Engineering, Finance, Marketing, and Product Problem Solving teams to deliver data-driven reliability insights and recommendations. The role also contributes to the development and enhancement of reliability tools, methods, and reporting processes.

Key Responsibilities

• Analyze current product reliability using statistical tools, warranty databases, and life data analysis techniques.
• Prepare and present reliability analysis reports, trends, findings, and recommendations to leadership and cross-functional teams.
• Support the evaluation of reliability impacts resulting from design changes and corrective actions.
• Identify adverse and favorable reliability trends across products and systems.
• Apply engineering and scientific methods to solve technical and reliability-related problems.
• Participate in product problem-solving activities using structured and data-driven methodologies.
• Support Product Failure Reporting and Corrective/Preventive Action Systems (FRACAS) activities.
• Work with cross-functional teams to identify root causes and implement reliability improvements.
• Develop and refine reliability analysis methods, tools, and reporting processes.
• Perform quantitative reliability analysis using statistical and life data analysis techniques.
• Support reliability test planning activities, including accelerated and system-level reliability testing.
• Handle and analyze large datasets to derive meaningful insights and recommendations.
• Collaborate with team members to resolve technical queries and support continuous learning and development initiatives.
• Ensure customer-focused solutions while contributing to product durability, reliability, and launch readiness objectives.

• Strong understanding of statistical methods including descriptive statistics, regression analysis, hypothesis testing, design of experiments, and probability distributions.
• Knowledge of product reliability engineering principles and reliability risk management practices.
• Understanding of product failure mode avoidance methodologies and root cause analysis techniques.
• Knowledge of Product Failure Reporting and Corrective/Preventive Action Systems (FRACAS).
• Ability to perform quantitative reliability analysis and interpret failure data.
• Understanding of reliability test planning and life data analysis methodologies.
• Knowledge of engines, components, systems, and failure modes preferred.
• Strong analytical and problem-solving capabilities with attention to detail.
• Proficiency in Microsoft Office applications including Excel, Word, and PowerPoint.
• Experience with Excel VBA and Power Pivot is desirable.
• Familiarity with statistical software tools such as Minitab, Weibull++, Windchill/Windsmith, JMP, and R Programming preferred.
• Knowledge of engine performance measurement and warranty data analysis is desirable.
• Strong capability in handling large datasets and deriving actionable conclusions.
• Effective communication and presentation skills.
• Ability to work collaboratively in cross-functional and global teams.
• Customer-focused mindset with the ability to deliver customer-centric solutions.
• Appreciation for diverse perspectives and global business environments.

• Basic relevant work experience preferred, including internships, co-op assignments, academic projects, or related industry experience.
• Experience in Service, Reliability, Quality, or Product Engineering functions is desirable.
• Exposure to warranty analytics, reliability analytics, or life data analysis preferred.

• Bachelor of Science, Master of Science, or equivalent technical degree in Engineering, Statistics, Reliability Engineering, Mechanical Engineering, or related technical discipline required.
• Six Sigma Yellow Belt or Green Belt certification preferred.