Warranty Management Jobs

Quality Data Analyst – Electrical Hardware Engineering, Warranty Analytics | KPI Automation | Design Assessment | AI Adoption

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Quality Data Analyst to join our team. This role will focus on leveraging data analysis and advanced techniques to support our Electrical Hardware Engineering, Warranty Analytics, KPI Automation, Design Assessment, and AI Adoption initiatives within the Automotive / Technology industry. The ideal candidate will possess a strong analytical background and a passion for driving data-driven decisions. Key Responsibilities This role will be responsible for a range of tasks including data analysis, visualization, and reporting to support quality initiatives. The candidate will be expected to identify trends, anomalies, and opportunities for improvement within warranty data, cost trends, and design performance. They will also be involved in the automation of KPI reporting and the implementation of AI -assisted analytics to enhance predictive quality and drive corrective action management . Required Skills & Qualifications Data Analysis: Demonstrated ability to extract, clean, and analyze large datasets to identify meaningful insights and trends. Data Visualization: Proficiency in creating compelling and informative data visualizations using tools such as Power BI and Excel Analytics to communicate complex information effectively. Dashboarding & KPI Reporting: Experience in designing and developing interactive dashboards and automated KPI reports to track key performance indicators and monitor quality metrics. Data Storytelling: Ability to translate data insights into clear and concise narratives that resonate with stakeholders and drive informed decision-making. SQL: Strong proficiency in SQL for data extraction, manipulation, and analysis. Python: Experience using Python for data analysis, scripting, and automation. Databricks: Familiarity with Databricks for data processing and analytics. Power Automate: Experience with Power Automate for workflow automation and data integration. Warranty Data Analytics: Expertise in analyzing warranty data to identify root causes of failures, predict future trends, and optimize product designs. Cost Trend Analysis: Ability to analyze cost trends to identify areas for cost reduction and process improvement. KPI Automation: Experience in automating KPI reporting and data collection processes. Multi-Source Data Integration: Ability to integrate data from multiple sources to create a comprehensive view of quality performance. AI-Assisted Analytics: Experience leveraging AI and Generative AI techniques to enhance data analysis and predictive modeling. Predictive Quality: Ability to apply statistical methods and machine learning algorithms to predict product quality and identify potential issues proactively. Anomaly Detection: Experience in identifying and investigating anomalies in data to uncover potential quality problems. Root Cause Analysis: Expertise in conducting root cause analysis to identify the underlying causes of quality issues. Corrective Action Management: Ability to develop and implement corrective action plans to address quality issues and prevent recurrence. DFMEA, DRBFM, APQP: Familiarity with DFMEA , DRBFM , and APQP methodologies for design and process improvement. Statistical Tools: Proficiency in using various statistical tools for data analysis and quality control. Structured Problem Solving: Ability to apply structured problem-solving techniques to identify and resolve quality issues. Process Optimization: Experience in identifying and implementing process optimization initiatives to improve quality and efficiency. Lean & Six Sigma: Knowledge of Lean and Six Sigma principles for continuous improvement. Quality Tools: Familiarity with various quality tools and techniques for data analysis and problem-solving. Issue Management: Ability to effectively manage and track quality issues to resolution. Warranty Management: Experience in managing warranty claims and analyzing warranty data. Field Quality Analysis: Ability to analyze field quality data to identify trends and patterns. Design Assessment: Ability to conduct design assessments to identify potential quality risks and improve product designs. Design Integrity: Understanding of design integrity principles and their impact on product quality. Design Robustness: Ability to evaluate and improve the design robustness of products. Lessons Learned Deployment: Ability to capture and deploy lessons learned from quality issues to prevent recurrence. Analytical Skills: Strong analytical skills with the ability to interpret data and draw meaningful conclusions. Problem-Solving Skills: Excellent problem-solving skills with the ability to identify and resolve complex quality issues. Data Interpretation Skills: Ability to accurately interpret data and translate it into actionable insights. Communication Skills: Excellent communication skills with the ability to effectively communicate technical information to both technical and non-technical audiences. Stakeholder Management: Ability to build and maintain relationships with key stakeholders . Leadership-Ready Presentation Skills: Ability to deliver compelling and persuasive presentations to senior management. Qualifications: Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline. What We Offer Experience Required: 5 - 10 years of relevant experience. Salary:

Quality Data Analyst – Electrical Hardware EngineeringWarranty Analytics | KPI Automation | Design Assessment | AI Adoption

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Quality Data Analyst to join our Electrical Hardware Engineering team. This role will focus on Warranty Analytics, KPI Automation, Design Assessment, and the adoption of AI within our quality processes. The ideal candidate will possess a strong analytical background and a passion for leveraging data to drive improvements in product quality and operational efficiency. This is an onsite position. Key Responsibilities The Quality Data Analyst will be responsible for a range of tasks including: Performing Data Analysis to identify trends and patterns in warranty data, product performance, and manufacturing processes. Developing and maintaining KPI Reporting dashboards to track key quality metrics and provide actionable insights to stakeholders. Automating KPI generation and reporting processes to improve efficiency and accuracy. Conducting Root-Cause Analysis of quality issues and collaborating with engineering teams to implement effective Corrective Action Management plans. Utilizing Data Mining techniques to uncover hidden patterns and insights within large datasets. Applying Statistical Analysis methods to evaluate product performance and identify areas for improvement. Leveraging AI-Assisted Analytics and Generative AI to enhance data analysis capabilities and identify potential quality risks. Implementing Process Optimization strategies to streamline quality control processes and improve overall efficiency. Required Skills & Qualifications To be successful in this role, candidates must possess the following skills and qualifications: A Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline. 5-10 years of experience in a data analysis role, preferably within the automotive or technology industry. This level of experience indicates a proven ability to independently manage complex data analysis projects and deliver impactful insights. Data Analysis : A strong foundation in data analysis techniques, including data cleaning, transformation, and exploration. Data Visualization : Proficiency in creating compelling and informative data visualizations to communicate insights effectively. Dashboarding : Experience in designing and developing interactive dashboards using tools such as Power BI . KPI Reporting : Ability to define, track, and report on key performance indicators ( KPIs ) to measure quality performance. Data Storytelling : Skill in translating complex data into clear and concise narratives that resonate with stakeholders. Quality Tools : Familiarity with various quality tools and methodologies, such as Lean and Six Sigma . Issue Management : Experience in identifying, tracking, and resolving quality issues in a timely and effective manner. Root-Cause Analysis : Ability to conduct thorough root-cause analyses to identify the underlying causes of quality problems. Corrective Action Management : Experience in developing and implementing corrective action plans to prevent recurrence of quality issues. Process Optimization : Ability to identify and implement process improvements to enhance quality control processes. Power BI : Proficiency in using Power BI to create interactive dashboards and reports. Excel Analytics : Advanced skills in Excel for data analysis and reporting. SQL : Strong proficiency in SQL for querying and manipulating data. Python : Experience using Python for data analysis and automation. Databricks : Familiarity with Databricks for big data processing and analysis. Power Automate : Experience using Power Automate for automating data-related tasks. Warranty Data Analytics : Experience analyzing warranty data to identify trends and patterns. Cost Trend Analysis : Ability to analyze cost trends to identify opportunities for cost savings. KPI Automation : Experience automating KPI generation and reporting processes. Multi-Source Data Integration : Ability to integrate data from multiple sources into a unified view. AI-Assisted Analytics : Experience leveraging AI to enhance data analysis capabilities. Predictive Quality : Ability to use data to predict potential quality issues. Anomaly Detection : Skill in identifying unusual patterns or outliers in data. Generative AI : Familiarity with Generative AI techniques for data analysis and insights. Process Automation : Experience automating manual processes to improve efficiency. Data Mining : Ability to extract valuable insights from large datasets. Statistical Analysis : Proficiency in applying statistical methods to analyze data. Quality Metrics : Understanding of key quality metrics and their application. Data Definitions : Ability to define clear and consistent data definitions. What We Offer We offer a competitive salary of .

Quality Data Analyst – Electrical Hardware Engineer

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Quality Data Analyst to join our Electrical Hardware Engineering, Warranty Analytics & Quality Automation team. This role will be instrumental in leveraging data to drive quality improvements, identify trends, and ensure the reliability of our products. The ideal candidate will possess a strong analytical background, a passion for data-driven decision-making, and a commitment to continuous improvement within the automotive and technology industries. Key Responsibilities The Quality Data Analyst will be responsible for a range of tasks related to data analysis, reporting, and process improvement. This includes conducting in-depth analysis of warranty data, identifying cost trends, and developing automated reporting solutions. The role will also involve collaborating with cross-functional teams to implement corrective actions and preventative measures to enhance product quality and reliability. Required Skills & Qualifications To be successful in this role, candidates must possess a combination of technical skills, analytical abilities, and a strong understanding of quality methodologies. Specific requirements include: Analytical Skills: Demonstrated ability to analyze complex data sets, identify patterns, and draw meaningful conclusions. Problem-Solving Skills: Proven ability to approach complex problems systematically, identify root causes, and develop effective solutions. Data Analysis: Proficiency in data analysis techniques, including statistical analysis and data mining. Data Visualization: Ability to create clear and compelling data visualizations to communicate insights effectively. Dashboarding: Experience in designing and developing interactive dashboards to monitor key performance indicators (KPIs). KPI Reporting: Ability to generate regular reports on KPIs and present findings to stakeholders. Data Storytelling: Ability to translate data into a narrative that is easy to understand and actionable. SQL: Strong proficiency in SQL for data extraction, manipulation, and analysis. Python: Experience using Python for data analysis and automation. Databricks: Familiarity with Databricks for large-scale data processing and analytics. Power Automate: Experience with Power Automate for automating data-related tasks and workflows. Warranty Data Analytics: Expertise in analyzing warranty data to identify trends and opportunities for improvement. Cost Trend Analysis: Ability to analyze cost data to identify trends and potential cost savings. KPI Automation: Experience in automating the generation and distribution of KPIs. Multi-Source Data Integration: Ability to integrate data from multiple sources into a unified view. AI-Assisted Analytics: Experience leveraging AI to enhance data analysis and predictive modeling. Predictive Quality: Knowledge of predictive quality techniques to anticipate and prevent quality issues. Anomaly Detection: Ability to identify unusual patterns or outliers in data that may indicate quality problems. Generative AI: Familiarity with Generative AI techniques for data analysis and problem-solving. Issue Management: Experience in tracking and resolving quality issues. Root-Cause Analysis: Proficiency in conducting root-cause analysis to identify the underlying causes of quality problems. Corrective Action Management: Ability to develop and implement effective corrective actions to prevent recurrence of quality issues. DFMEA: Understanding of DFMEA (Design Failure Mode and Effects Analysis) principles. DRBFM: Understanding of DRBFM (Design Review Based on Failure Mode) principles. APQP: Understanding of APQP (Advanced Product Quality Planning) principles. Statistical Tools: Proficiency in using various statistical tools for data analysis and quality improvement. Structured Problem Solving: Ability to apply structured problem-solving methodologies to address quality challenges. Power BI: Proficiency in Power BI for data visualization and reporting. Excel Analytics: Strong Excel skills for data analysis and reporting. Process Optimization: Ability to identify and implement process improvements to enhance quality and efficiency. Six Sigma: Knowledge of Six Sigma methodologies for process improvement. Quality Tools: Familiarity with various quality tools and techniques. Lean: Understanding of Lean principles for waste reduction and process improvement. Education: A Bachelor’s degree in Electrical Engineering, Electronics Engineering, Automotive Engineering, Data Analytics, Computer Science, or a related engineering discipline is required. Experience: The ideal candidate will have 5-10 years of experience in a related field, demonstrating a strong track record of success in data analysis and quality improvement. This level of experience is expected to provide a solid foundation for independent work and leadership within a team. What We Offer Salary: