Senior Application Engineer
Current- Responsible for building Machine Leaning models
- Designed and implemented a machine learning system that predicts hardware malfunction with more than 80% accuracy
- Conducted root cause analysis using statistical techniques to identify factors contributing toproduction inefficiencies and implement corrective actions.
- Conducted Data Preprocessing and exploratory data analysis (EDA) to identify trends, patterns, andoutliers within manufacturing data, leading to actionable insights for process optimization.
- Generated statistical reports and visualizations, providing key insightsApplied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance inmanufacturing data, improving model performance and.
- Utilized Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensionaldatasets, capturing the most relevant features while preserving variance and reducing computationalcomplexity.
Oct 2021 - Present
