Neha User

I am an Electronics and Communication Engineering graduate with hands-on process engineering experience in smartphone assembly and testing. I contributed in a FATP department, preparing process documentation and supporting continuous improvement and new product introduction.

I have applied Python and C++ to data analysis and machine learning projects, including a published deep-learning study on brain tumor detection. I implemented EfficientNetB0 for classification and Mask R-CNN for segmentation, and used TensorFlow and Keras extensively.

I collaborated with cross-functional teams to implement lean practices, reduce defects, and optimize manufacturing efficiency and quality. I also performed consumer insights analysis using Python, Pandas, Matplotlib, OpenCV, NumPy and scikit-learn to produce actionable recommendations.

I am driven to bring technical, analytical, and process-improvement skills to real-world challenges, continuously learn, and apply research-backed machine learning approaches to practical problems.