Sr Data Science Engineer

**Sr Data Science Engineer**:

- Experience Level: Individual Contributor- Job Type: Full-Time- Location: Singapore - Singapore, SG- Requisition ID: 8439- Applied Data Science on Test Yield Analysis & Improvement Opportunity
- Product Test Yield and Key Test Parameters Analysis in relationship & correlation to Fab WET data or Wafer Probe Die Sort Data
- Develop a predictive model of product test yield based on available key Fab, Sort monitoring data
- Leveraging on advance data analytics and Artificial Intelligence technique to enable speed of detection, prevention and prediction
- Collaborate with respective product owner PE to drive yield improvement opportunity based on big data analysis or specific excursion with timely detection of abnormality
- Quality Monitoring Data & Customer Complaint Analysis in relations to Test Information & Manufacturing Process Data
- Analyze real-time monitoring data abnormality (Maverick) detection & prevention measures effectiveness for applicable semiconductor manufacturing processes.
- Leverage predictive analytics to trigger interventions that reduce the risk of yield loss or quality issues.
- Collaborate with respective product owner PE to drive out-quality improvement opportunity based on big data analysis & predictive quality risk
- Collaborate with Manufacturing Site to Drive Data-Driven Process Optimization for Yield and Quality Improvement
- Work closely with on-site Product, Test or Process engineers to analyze production data, identify trends, and optimize manufacturing processes.
- Use data science techniques to reduce variability and improve overall product quality.
- Failure Mode Analysis & Customer Risk/RMA Mitigation
- Enhance product quality & reliability by developing predictive models that assess the potential quality risk to customers. Use insights from these models for disposition decision making to ensure compliance with quality standards.
- Collaborate with quality, process, product engineer on in-depth failure mode and effects analysis (FMEA) using historical and real-time data to predict and mitigate potential defects.
- Collaborate with manufacturing teams to implement corrective actions based on predictive insights.
- Additional Responsibilities:

- Track wafer and package yield trends on a daily basis
- Leverage 2DID and Die Level Traceability to perform novel analyses and move yield/quality issues upstream to wafer level whenever possible
- Identify opportunities for cost reduction
- Generate analyses aimed at highlighting device sensitivities and interactions between design/fab/assy/test
- Leverage all available test points to detect anomalies, shifts, drifts, outliers, maverick events as close to the Point of Occurrence as possible
- Identify opportunities for cost reduction
- Monitor/Report on KPI's and facilitate data-driven decision making

**Requirements**:

- Domain Expertise:

- Bachelor’s or Master degree in Data Science, Computer Science, Electrical Engineering, or a related field, with specialized knowledge in semiconductor manufacturing processes. Experience in predictive analytics for yield and quality in semiconductor fabs is highly desirable
- Technical Skills:

- Proficiency in programming languages such as Python or R, with preferred experience in machine learning libraries and statistical modeling. Strong background in data analysis, including the use of big data technologies and cloud platforms
- Experience with Semiconductor Manufacturing:

- At least 3 years of experience working in semiconductor manufacturing, particularly in roles focused on process monitoring, yield improvement, and quality control. Familiarity with Fab, Die probe, and Assembly processes is essential
- Analytical and Predictive Modeling Expertise:

- Proven track record in developing and deploying predictive models for yield analysis and quality assurance. Experience with tools like JMP, Minitab, or similar for statistical process control (SPC) and predictive maintenance is a plus.
- Cross-Functional Collaboration and Communication:

- Strong communication skills to effectively collaborate with process engineers, quality assurance teams, and other stakeholders. Ability to translate complex data insights into actionable strategies that improve product yield and quality.

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