Shimone Pareshkumar Vyas

| Salesforce Quality Assurance Lead Consultant - Ex-Deloitte | Data Scientist Intern |

Dynamic and results-driven professional combining expertise as a SaaS-based Quality Assurance Engineer and a Computer Vision Specialist. Proven track record of leading teams to achieve 75% high-quality software with minimal defect leakage, recognized with 4 prestigious awards for excellence in Quality Assurance. Adept in test automation, defect tracking, and implementing rigorous quality standards that enhance operational efficiency. Currently expanding my skill set through an online Data Science course, focusing on Machine Learning and Image Processing. Achieved 64% validation accuracy with a Convolutional Neural Network model during my thesis, showcasing my ability to generate cutting-edge IT solutions. Committed to leveraging analytical skills and strategic leadership to drive performance and deliver exceptional results in fast-paced environments. Kindly peruse my LinkedIn testimonials to learn more about my career pursuits and accomplishments in relevant fields.

• Salesforce Administrator (06/2023)
• Microsoft Azure Admin Associate Microsoft (04/2022)
• TOSCA Automation Specialist 1 Tricentis (10/2021)
• TOSCA Automation Specialist 2 Tricentis (10/2021)
• Testing in Agile (09/2021)

Project: Analysis of Neural Network Architecture and Machine Learning for Emotion Detection Duration: May 2024 – Sept 2024

💠 Engineered Advanced Models: Reliable models for emotion detection were constructed employing
KNN, CNN, and Random Forest algorithms against different emotions.

💠 Optimized Performance with Hyperparameter Tuning: The model's efficacy was enhanced by
complying with industry best practices and achieving superior performance metrics. Strategic
hyperparameter tuning was leveraged by altering parameters namely Epochs, number of layers,
kernel size, number of filters, k-value, nature of optimizers, and activation functions.

💠 Integrated Cutting-Edge Research: Performed thorough analysis of cutting-edge Computer Vision
research, and literary works, incorporating current trends to improve project outcomes.

💠 Employed Advanced Optimizers and Activation Functions: To increase learning effectiveness and
model correctness, CNN models were fitted with optimizers like ‘Adam’, ‘RMSprop’, and ‘SGD’ in
addition to activation functions like ‘ReLU’ and ‘Sigmoid’.

💠 Applied Powerful Libraries: Streamlined development processes and improved analytical capabilities
by working effectively with key libraries namely NumPy, Keras, TensorFlow, RandomUnderSampler,
Scikit-Learn, etc.,

💠 Evaluated Model Performance with Precision Metrics: The framework’s proficiency was evaluated
utilizing crucial metrics like Accuracy, F1-score, Re-call, and Precision to guarantee thorough
performance evaluation. Confusion Matrices and graph plots yielded visually meticulous findings.

💠 Comprehensive Comparative Analysis: Performed comprehensive comparative review on bifurcated
emotion and multi-class datasets, providing insights that guided model selection and improvement
tactics. Multi-class yielded 26% upon applying the ‘Tanh Activation’ whilst Binary bifurcated class
yielded 64% for ‘Tanh’ and ‘ReLu’ Activation functions and 63% for the ‘Sigmoid’ Activation function.

Deloitte USI :

• Applause Award, Nov 2022
• Applause Award, Jun 2022
• Spot Award, Jun 2021
• Spot Award, Jan 2020