Sr Principal, Quality Engineer

The ideal candidate should have a strong analytical mindset, a deep understanding of manufacturing processes, and excellent communication skills to effectively interact with customers and lead the projects with internal team.

Key Responsibilities:

1. Data Analysis:Assess field application performance data, evaluating how products perform in real-world conditions and identifying areas for enhancement. Collect, analyze, and interpret production yield data to identify trends, patterns, and opportunities for improvement in manufacturing processes. Use statistical tools and techniques (e.g., regression analysis, hypothesis testing) to assess key metrics such as yield rates, defect rates, and process efficiency.

Generate regular reports and/or dashboards to communicate production performance and field application insights to the internal team and customers.

2. Customer Interface:Collaborate directly with customers to gather feedback on field applications and product performance. Provide data-driven insights and recommendations to customers based on analysis of field performance data. Work with the customer service and sales teams to ensure that customers’ concerns related to product quality and field application are addressed promptly. Present data findings to customers in a clear, understandable format, supporting decision-making with data-driven insights.

3. Cross-Functional Collaboration:Partner with production, engineering, quality assurance, and R&D teams to translate data insights into actionable process improvements. Support the development and implementation of strategies to improve yield and reduce waste, defects, and costs. Communicate complex data analysis results to non-technical stakeholders through presentations and reports.

4. Continuous Improvement:Monitor and analyze key performance indicators (KPIs) related to production and field applications to drive continuous improvement efforts. Identify bottlenecks and inefficiencies in the production process and recommend corrective actions based on data analysis. Lead or support special projects aimed at improving overall operational efficiency and product performance in the field.