Design Assessment 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:

Virtual Analysis Engineer L2 Safety

RLE India Private Limited•

Chennai

•full time

Job Summary We are seeking a skilled and motivated Virtual Analysis Engineer L2 Safety to join our team. This is a remote position within the Automobile industry. The ideal candidate will possess a strong analytical background and experience in performing virtual analysis to ensure the safety and performance of our vehicles. Key Responsibilities The Virtual Analysis Engineer L2 Safety will be responsible for conducting comprehensive CAE analysis , contributing to design reviews , and identifying opportunities for structural improvements . This role requires a proactive approach to problem-solving, clear communication of findings and lessons learned , and a commitment to teamwork . Regular project updates will be essential, ensuring stakeholders are informed of progress and any potential challenges. Required Skills & Qualifications Education: A B.E , B.Tech (Mechanical, Automobile), M.E , or M.Tech degree is required. Experience: 3 - 7 years of experience in a related field is expected, demonstrating a solid foundation in virtual analysis and a proven ability to contribute effectively to a team. CAE Analysis: Proficiency in performing CAE analysis , including Occupant simulations and analysis at both Vehicle level and Sled level . Model Refinement & Quality Assessment: Experience in model refinement and rigorous quality assessment of simulation results. Analytical & Problem-Solving Skills: Demonstrated analytical skills and problem-solving skills , with the ability to identify root causes and propose effective solutions. Engineering Fundamentals: A strong understanding of Classical Mechanics , Applied Mechanics , Strength of Materials , and Physics fundamentals . Human Body Modeling: Experience with Human Body Model (HBM) simulations. Scripting & AI Tools: Familiarity with scripting and the application of AI tools in the analysis process. Materials Expertise: Knowledge of Metals and Composite parts , including failure analysis techniques. Cross-Functional Collaboration: Ability to effectively collaborate with cross functional performance requirements teams. Design & Presentation: Ability to contribute to design proposals and deliver clear and concise presentations . Professional Attributes: Demonstrated responsibility , ownership , and a strong learning aptitude . Communication Skills: Excellent communication skills , both written and verbal. Logical Reasoning: Strong logical reasoning abilities. What We Offer 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 .

CAE Analyst

RLE India Private Limited•

Chennai

•Full-time

CAE Analyst Job Description Job Summary We are seeking a skilled and experienced CAE Analyst to join our team in Chennai. This role is crucial for supporting our Automotive / Mechanical Engineering initiatives, ensuring the quality and durability of our components and systems. Key Responsibilities The CAE Analyst will be responsible for performing a wide range of CAE simulations and analyses to support product development and validation. This includes, but is not limited to, conducting structural analysis , stress analysis , and fatigue life prediction . The role will also involve root cause analysis and failure analysis to identify and resolve potential issues. The analyst will work closely with design and validation teams to ensure that designs meet performance and durability requirements. Required Skills & Qualifications The ideal candidate will possess a strong foundation in Mechanical Engineering and a proven ability to apply CAE principles to solve real-world engineering problems. A B.E / B.Tech / M.E / M.Tech degree is required. Non-linear FE analysis: Proficiency in performing advanced finite element analyses, including non-linear simulations to accurately predict component behavior under complex loading conditions. Abaqus: Demonstrated experience using Abaqus for conducting FE simulations. FE model building: Ability to create accurate and efficient FE models representing complex geometries and boundary conditions. Meshing: Expertise in generating high-quality meshes using industry-standard tools. HyperMesh: Experience utilizing HyperMesh for efficient mesh generation and model preparation. ANSA: Familiarity with ANSA for pre- and post-processing of FE models. Fatigue life prediction: Ability to perform fatigue life prediction using established methodologies and software. FEMFAT: Experience with FEMFAT for fatigue analysis and damage assessment. Structural analysis: A strong understanding of structural analysis principles and their application to automotive components. Stress analysis: Ability to perform detailed stress analysis to identify areas of high stress concentration and potential failure. Material modeling: Knowledge of material modeling techniques and their impact on simulation results. Contact definitions: Ability to accurately define and implement contact definitions in FE models. Reliability assessments: Experience in performing reliability assessments to evaluate the long-term performance of components. Durability assessments: Ability to conduct durability assessments to ensure components can withstand expected usage conditions. Root cause analysis: Proven ability to perform root cause analysis to identify the underlying causes of failures. Failure analysis: Experience in conducting failure analysis to determine the mechanisms of failure. Automotive engineering: A solid understanding of Automotive engineering principles and practices. Quality processes: Familiarity with quality processes and their application to product development. Robust design methodologies: Knowledge of robust design methodologies to create designs that are insensitive to variations in manufacturing and operating conditions. Manufacturing best practices: Understanding of manufacturing best practices and their impact on component performance. Transmission & driveline systems: Familiarity with Transmission & driveline systems and their components. Gears, Shafts, Clutches, Differentials, Propshafts, Half-shafts, Bearings: Knowledge of the design and analysis of these key components. HyperWorks: Experience with HyperWorks for CAE simulation and analysis. Fatigue life/Damage assessment: Ability to perform fatigue life/Damage assessment to predict component lifespan. Boundary conditions: Ability to accurately define and apply appropriate boundary conditions in FE models. Dynamic loading scenarios: Experience in simulating dynamic loading scenarios to evaluate component response to transient loads. Gear strength: Ability to assess gear strength and durability. Shaft durability: Ability to assess shaft durability under various loading conditions. Road load data: Experience in utilizing road load data for simulation. Simulation load cases: Ability to develop and apply appropriate simulation load cases . Physical test data: Ability to correlate simulation results with physical test data . Refine models: Ability to refine models based on simulation results and experimental data. Material properties: Understanding of material properties and their impact on simulation results. Manufacturing processes: Familiarity with manufacturing processes such as Forging, Casting, and Heat treatment . Component durability: Ability to assess component durability under various operating conditions. CAE simulation tools: Proficiency in using various CAE simulation tools . Customer usage profiles: Ability to incorporate customer usage profiles into simulation models. Duty cycles: Ability to define and apply appropriate duty cycles in simulations. Design robustness: Ability to assess and improve design robustness . DFMEA: Familiarity with DFMEA (Design Failure Mode and Effects Analysis). DVP&R: Understanding of DVP&R (Design Verification Plan and Release). Experience Required: The role requires 4 - 9 years of relevant experience in CAE analysis, preferably within the Automotive or Mechanical Engineering industry. Candidates with a strong track record of solving complex engineering problems and delivering high-quality results are highly encouraged to apply. What We Offer Salary: ₹10 LPA - ₹12 LPA