Data Analysis Tools Jobs

Project Management

SACHA Engineering•

Chennai

•Full-time

Project Management Job Description Job Summary We are seeking a skilled and motivated Project Management professional to join our team in Chennai. This role will be instrumental in managing and coordinating various projects within our IT / Technology environment. Key Responsibilities The Project Management role will involve a range of responsibilities focused on ensuring projects are completed efficiently and effectively. This includes utilizing Project Management Tools and Project Management Techniques to plan, execute, and monitor project progress. The successful candidate will be expected to adhere to Industry Standards and contribute to a positive project outcome. Required Skills & Qualifications Project Management Tools: Proficiency in utilizing various Project Management Tools to facilitate project planning, task assignment, and progress tracking. Project Management Techniques: A strong understanding of Project Management Techniques , including but not limited to Agile, Waterfall, or hybrid methodologies, to effectively manage project scope, timelines, and resources. Industry Standards: Familiarity with relevant Industry Standards to ensure project deliverables meet quality and compliance requirements. Automotive-Specific Terminology: A working knowledge of Automotive-Specific Terminology is highly desirable, allowing for effective communication and understanding within the context of our projects. Product Development Lifecycles: Understanding of Product Development Lifecycles to ensure alignment with overall business objectives. Data Analysis Tools: Ability to leverage Data Analysis Tools to extract meaningful insights from project data, identify trends, and inform decision-making. Excel: Advanced proficiency in Excel for data manipulation, analysis, and reporting. VBA: Experience with VBA for automating tasks and enhancing Excel functionality. PowerBI: Ability to create and utilize PowerBI dashboards to visualize project data and communicate key performance indicators. Organizational Skills: Exceptional Organizational Skills to manage multiple tasks, prioritize effectively, and maintain meticulous project documentation. Analytical Skills: Strong Analytical Skills to assess project risks, identify potential issues, and develop effective mitigation strategies. Problem-Solving Skills: Demonstrated Problem-Solving Skills to address challenges proactively and implement creative solutions. Written Communication Skills: Excellent Written Communication Skills to prepare clear and concise project documentation, reports, and correspondence. Verbal Communication Skills: Effective Verbal Communication Skills to communicate project status, updates, and concerns to stakeholders. PowerPoint Skills: Proficiency in PowerPoint to create compelling presentations for project updates and stakeholder meetings. Experience Required: We are seeking candidates with 1 - 5 years of experience in a related field. This level of experience indicates a foundational understanding of project management principles and the ability to contribute effectively to a team. The expectation is that candidates will be able to work independently on assigned tasks and contribute to the overall success of the project. What We Offer Salary: [Salary Information Not Provided]

Product Owner

SACHA Engineering•

Chennai

•full time

Job Summary We are seeking a skilled and experienced Product Owner to join our dynamic team. This is a hybrid role requiring a blend of in-office collaboration and remote work flexibility. The ideal candidate will possess a strong understanding of the IT / Technology industry and a proven ability to manage product development from conception to delivery. Key Responsibilities The Product Owner will be responsible for defining and prioritizing product features, ensuring alignment with business goals, and driving the development process. This includes managing the product backlog , collaborating with stakeholders, and ensuring the delivery of high-quality solutions. Required Skills & Qualifications Stakeholder Management Skills: Ability to effectively communicate and collaborate with diverse stakeholders, including business users, development teams, and leadership, to gather requirements and ensure alignment on product vision. Agile Methodologies: Demonstrated proficiency in Agile development practices, including Scrum and Kanban , to facilitate iterative development and continuous improvement. User Story Creation: Ability to translate business requirements into clear and concise user stories that accurately reflect user needs and acceptance criteria. Backlog Prioritization: Skill in prioritizing the product backlog based on business value, urgency, and dependencies, ensuring the most important features are delivered first. Sprint Delivery: Experience in managing sprint planning, execution, and review, ensuring timely and efficient delivery of product increments. Requirements Analysis: Ability to conduct thorough requirements analysis to identify and document business needs, ensuring a clear understanding of the problem being solved. Documentation: Proficient in creating and maintaining comprehensive product documentation, including requirements specifications, user manuals, and training materials. Validation & UAT Support: Experience in supporting product validation and UAT (User Acceptance Testing) processes to ensure product quality and user satisfaction. Shipping Domain & Vessel Operations: A strong understanding of the shipping domain , including vessel scheduling , network planning , and voyage planning . Service Creation & Berth Planning: Experience in service creation and berth planning within a shipping or logistics environment. Port Rotation Planning & ETA/ETD Planning: Ability to plan port rotations and accurately forecast ETA/ETD (Estimated Time of Arrival/Departure) schedules. Port Cut-Offs & Transit Time Calculation: Knowledge of port cut-offs and the ability to accurately calculate transit times . Schedule Versioning & Disruption Handling: Experience in managing schedule versioning and effectively handling disruption scenarios. Schedule Publication Workflows: Familiarity with schedule publication workflows and ensuring timely and accurate dissemination of schedule information. Technical Skills: Proficiency in SQL for data retrieval and analysis, with a solid database understanding . Collaboration Tools: Experience using JIRA and Confluence for project management and knowledge sharing. Productivity Tools: Proficient in using MS Word for documentation and reporting. Freight Forwarding & Intermodal Transport: Understanding of freight forwarding and intermodal transport processes. Shipping Documentation & Customs Regulations: Knowledge of shipping documentation and customs regulations . Cargo Handling: Familiarity with cargo handling procedures. Wireframes & Axure Prototyping Tools: Ability to create wireframes and utilize Axure Prototyping Tools for user interface design. FIGMA & PowerDesigner: Experience with FIGMA and PowerDesigner for design and modeling. PowerDesigner Viewer & LDM: Familiarity with PowerDesigner Viewer and LDM (Logical Data Model). Product Owner: Demonstrated experience and understanding of the Product Owner role and responsibilities. Experience Required: The ideal candidate will possess a minimum of 8-15 years of relevant experience in product management or a related field. This level of experience indicates a strong foundation in product development principles and the ability to lead and influence cross-functional teams. What We Offer Salary: 35 LPA

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:

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