Performance Evidence Definition: You translate system-level and scale-up questions into clear experimental objectives and KPIs, define required datasets and operating windows, and ensure that testing generates reliable, traceable, and decision-ready performance evidence.
Experimental Data Analysis: You process and structure KPI-based datasets with statistical rigor, identify trends and operational limits, evaluate thermal or transport constraints, and quantify uncertainty to deliver robust insights from experimental results.
Model Validation & Risk Identification: You compare model predictions with experimental outcomes, identify calibration gaps and model assumptions needing refinement, diagnose deviations, and propose targeted improvements while contributing to standardized validation workflows.
Cross-Functional Technical Alignment: You work closely with Process Development and Technology teams to align KPIs, test plans, and interpretations, while ensuring clear technical communication and maintaining well-structured documentation.
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