Product and Quality Performance Engineer for an Environmental Sensor company in Newark, Ca.
Looking for a self-motivated individual to spearhead yield, quality, performance and cost improvement of leading-edge environmental sensing systems from new product introduction through high-volume manufacturing through statistical analysis of data.
- Hands-on drive company profitability through product distribution tightening, cost of non-quality reduction (yield improvement), and productivity and quality improvement based on the analysis of in-line SPC, final test, reliability, and physical analysis data.
- Map yield losses and reliability failures unambiguously back to root cause (“Red “X”) based on statistical analysis of data. Prioritize and drive corrective action plans in conjunction with in-house and supply-chain partner engineering, quality, production, and test teams.
- Define and implement appropriate test processes and equipment capability studies for first-in-kind environmental sensor test systems in conjunction with test, process, and product engineers. Identify the statistical techniques required for establishing, controlling and verifying test and in-line SPC capability and characteristics in conjunction with test and process engineers.
- Support operations and test excellence improvements in all areas as required.
- Min 3 years work experience in an industrial setting. Very strong statistical analysis and experimental design capability.
- Solid competency in extracting and organizing data and statistics in SQL.
- Strong working knowledge of Python, JMP, R, or other statistical languages. Willingness to learn Python.
- Strong collaborative and team work attributes with active communication and good documentation skills.
- Hands-on analytic personality.
- Effective, collaborative, 360° management skills.
- Ability to support international travel up to 25% of the time. May require travel 1 week per month.
- Degree in a Quantitative field (Statistics / Mathematics / Physics / Engineering / Accounting / Actuarial Sciences) or relevant experience
- Working knowledge of data mining algorithms including classifications techniques, probability networks, association rules, supervised and unsupervised clustering and Generalized Linear Regression.
- Relational Database knowledge (SQL) with the ability to mine the data for anomalies, trends, outliers and correlations.
- Semiconductor assembly process engineering experience.
Type: Full Time
Location: Newark, Ca