Faiza Kashish
@user.2517218
Corporate Strategy at Granules India Limited
Jamshedpur, Jharkhand
Faiza Kashish is a B.Tech graduate in Electrical Engineering from the Indian Institute of Technology Kharagpur (2024) with strong technical skills in C++, Python, SQL, and machine learning. She is currently working in Corporate Strategy at Granules India Limited, reporting to the Chief Strategy and Sustainability Officer. She has interned as a Data Analyst at multiple organizations including Foxmula, Mind Harmonics, and IIT Kharagpur, and has led research projects in NLP and cyclone prediction using neural networks.
Experience
Corporate Strategy
Granules India Limited
Reported to the Cheif strategy and sustainability officer in the pharmaceutical company. Analysed the sustainibility reports of various companies and gathered trends from them. Built a ESG website to gather data about the company ESG policies.
Data Analyst Intern
Mind Harmonics
Built Python-based ML model to forecast house prices using key features (area, bedrooms, location). Achieved R2 score of 0.85. Expertly managed missing values, scaled features, and crafted ’Price per Sq. Ft.’ metric to enhance model’s accuracy. Led project from data collection to model deployment. Utilized pandas, scikit-learn, matplotlib for analysis, modeling, and visualization.
Research Intern
IIT Kharagpur
Created MATLAB code to process frequency domain data from Excel, optimizing transfer function coefficients using fminsearch. Transformed decibel-magnitude and phase-degree data to linear and radians, analyzing real-world datasets. Utilized MATLAB to develop an automated transfer function fitting solution, showcasing data-driven engineering skills.
Product Intern
Edudigm
Restructured the supply chain system of the company using product roadmaps. Applied economy of scale theory to enhance the efficiency of system. Collaborated with the COO and founders to drive growth, focusing on streamlining operations and supply chain management.
Data Analyst Intern
Foxmula
Executed a Binary Classifier using Random Forest, BERT and LinearSVC with the accuracy of 81.9%, 87.8% and 91.7%. Developed an API server on python using Fast API that can accept an english text and respond with the predicted sentiment. Led project from data collection to model deployment. Utilized pandas, scikit-learn, matplotlib for analysis, modeling, and visualization.
Education
Indian Institute of Technology, Kharagpur
Btech
Electrical engineering