Experienced Data Scientist with an M.S. in Data Science, specializing in Generative AI. Skilled in supervised/unsupervised learning, regression, time series analysis, classification, and clustering. Proficient in Python, SQL, Seaborn, and Matplotlib, with expertise in Deep Neural Networks, TensorFlow, PyTorch. Currently, at Studiovity, researching and developing innovative features using Generative AI techniques. Past roles include predicting loan approvals and speech emotion at CodeClause and prognostics in predictive maintenance at iNeuron AI. Strong in EDA, feature engineering, model building, evaluation, and hyperparameter tuning. Excels in solving real-world challenges and delivering high-impact solutions.

Sep 2023 - Current

• Spearheading research and development in Generative AI techniques, including stable diffusion, Google Dream Booth, Text Inversion, LoRa, and Perfusion.
• Innovating to introduce groundbreaking features for products.

Aug 2023 - Sep 2023

• Developed machine learning models for predicting loan approvals and speech emotion, contributing to real-world problem-solving in financial and audio data domains.
• Conducted data preprocessing, feature engineering, and model selection to enhance accuracy and effectiveness.
• Implemented classification algorithms and regression techniques using Python, scikit-learn, and TensorFlow.
• Gained hands-on experience in handling imbalanced datasets and optimizing model parameters.

Feb 2023 - Sep 2023

• Contributed to predictive maintenance projects, focusing on prognostics and health management.
• Analyzed run-to-failure simulation data from turbofan jet engines provided by The Prognostics CoE at NASA Ames.
• Developed models to predict the remaining useful life (RUL) of each engine, aiding in asset state anticipation and downtime prevention in industrial settings.

• M.S. in Data Science, Liverpool John Moores University, Aug 2022 - Feb 2024

  • Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, Deep Learning.

• Executive PG Programme in Data Science, International Institute of Information Technology, Bangalore (IIIT-B), Aug 2022 - Aug 2023

  • Statistics, Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data Analytics, Deep Learning.

• Bachelor of Engineering, Pune University

1. Predictive Maintenance System for Turbo Engines
   • Feb 2023 - Aug 2023
   • The Predictive Maintenance System is designed to predict the remaining useful life (RUL) of turbo engines to anticipate asset state, avoid downtime, and prevent breakdowns. The system uses machine learning algorithms to analyze sensor data from turbofan jet engines and provides real-time RUL predictions.

1. Melanoma Detection - Multiclass classification model using a custom convolutional neural network in TensorFlow

   • Mar 2023 - Jun 2023
   • Developed a CNN-based model to accurately detect melanoma, a type of deadly skin cancer. The model helps in reducing manual effort needed for diagnosis.

2. Customer Churn Prediction

   • Jan 2023 - Feb 2023
   • Predicted customer churn by performing data cleaning, EDA, feature engineering, feature selection, model building, evaluation, and hyperparameter tuning using Random Forest Classification and Logistic Regression.

3. Credit Risk Analysis

   • Sep 2022 - Oct 2022
   • Conducted risk analytics in banking and financial services to minimize the risk of losing money while lending to customers.

4. Prediction of Demand for Bike Sharing Application

   • Dec 2022 - Jan 2023
   • Developed a multiple linear regression model to predict demand for shared bikes. Employed feature selection, regression modeling, and model evaluation to assist management in understanding demand dynamics.

Feel free to reach out and connect with me on LinkedIn or email. Let's collaborate and explore the fascinating world of data science!

LinkedIn Email: shrutidhange@gmail.com