• Partner with cross-functional teams (device engineering, test, yield, and customer-facing consultants) to define data sources, characterization objectives, analysis methodologies, and deliverable formats aligned to project timelines.
• Apply and continuously refine standardized electrical characterization and parametric analysis methodologies while adapting to technology node, device architecture, and customer-specific requirements.
• Analyze and interpret parametric test data (e.g., IV, CV, reliability, variability metrics), identifying sources of variation across process, design, test conditions, and data integrity. Proactively troubleshoot data anomalies including probe/test issues, data pipeline errors, and process-induced variability.
• Collaborate closely with test engineers, product engineers, and design teams to debug silicon issues, validate test structures, and ensure data quality and measurement accuracy.
• Develop, customize, and optimize analysis workflows, scripts, and reporting templates (e.g., JMP, Python, MATLAB) to improve efficiency, scalability, and repeatability of characterization analysis.
• Perform rigorous quality checks on analysis outputs to ensure technical accuracy, statistical relevance, and clarity of insights for engineering and customer stakeholders.
• Benchmark results against historical data, baseline models, and expected device behavior; identify deviations, trends, and early indicators of yield or reliability risks.
• Synthesize complex datasets into clear, actionable insights and communicate findings through well-structured reports and presentations to internal teams and external clients.
• Lead or support advanced analyses including DOE (Design of Experiments), correlation studies across multiple data sources (fab, test, reliability), and time-based performance tracking.

• Electrical Characterization (CHAR) ExpertiseStrong hands-on experience with parametric test data (IV, CV, Idsat, Vt, leakage, variability, reliability) and deep understanding of device behavior across silicon.
• Semiconductor Device Physics & Process KnowledgeSolid grounding in MOSFET/device physics and process integration, with the ability to link electrical signatures to process variations and yield impact.
• Advanced Data Analysis & StatisticsProven capability in statistical analysis, variability characterization, and Design of Experiments (DOE) to diagnose complex yield and performance issues.
• Programming & Data ToolsProficiency in tools such as Python, JMP, MATLAB, or similar for data mining, automation, and large dataset analysis.
• Root Cause Analysis & DebuggingDemonstrated ability to troubleshoot electrical anomalies across test, probe, and process domains; isolate root causes and drive resolution.
• Cross-Functional CollaborationExperience working with test, product, yield, and design teams to close the loop between silicon data and engineering actions.
• Data Integrity & ValidationStrong attention to data quality, with the ability to identify inconsistencies from test setup, data pipelines, or measurement artifacts.
• Technical CommunicationAbility to distill complex datasets into clear insights, reports, and presentations for both internal stakeholders and customers.

Nice to have-

• Advanced Node Experience (FinFET / GAA / sub-10nm)Familiarity with variability challenges, short-channel effects, and reliability concerns specific to leading-edge technologies.
• Reliability & Failure Analysis KnowledgeExposure to TDDB, BTI, HCI, EM, and other reliability mechanisms, with ability to correlate parametric shifts to long-term behavior.
• Test & Measurement SystemsExperience with ATE platforms, wafer probe, parametric testers, and test structure design.
• Scripting & Automation at ScaleAbility to build scalable data pipelines, dashboards, or automated reporting solutions for high-volume manufacturing environments.
• Data Visualization & BI ToolsExperience with tools like Tableau, Spotfire, or similar for interactive analysis and stakeholder reporting.
• Customer-Facing ExperienceAbility to present technical findings directly to customers and support external engagements.
• Machine Learning / Advanced Analytics (Emerging)Exposure to predictive modeling or anomaly detection techniques applied to semiconductor data.