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 .
