About

Pranathi Chunduru is a Research Data Scientist with almost 6 years of experience in applied machine learning, statistical modeling, deep learning, and predictive modeling. She has worked in diverse industries, including Semiconductor, Medical Devices, and Healthcare, to help businesses build data-driven decision-making tools. Pranathi is well-versed in machine learning algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Gradient Boost Machine, XGBoost, K Means Clustering, and Neural Network. She is proficient in programming languages such as Python (NumPy, SciPy, Pandas, Scikit-Learn), R, MATLAB, SAS, and SPSS. She has experience working with big data tools and ML platforms such as PySpark, TensorFlow, Pytorch, and Keras. Pranathi is also experienced in distributed/cloud computing using AWS (SageMaker), IBM-Watson, Docker, Git, and NVIDIA-CUDA. Pranathi's data analysis techniques include statistics, machine learning, data mining, data modeling, predictive modeling, data visualization, time series forecasting, natural language processing, and survival analysis. She is always eager to learn and explore new frontiers in Machine Learning & Deep Learning. Currently, Pranathi is working as a Senior Machine Learning Scientist at Johnson & Johnson's Robotics and Digital Solutions division. Previously, she worked as a Sr Data Science Analyst at the University of California San Francisco, where she built interpretable predictive models and clinical decision tools. She has also participated in Kaggle competitions, where she worked on projects such as Human Activity Recognition with Smartphone, Neural network regression to predict unified Parkinson’s disease rating scale, and Natural Language Processing on Yelp restaurant reviews. Pranathi holds a Master of Science (M.S.) degree in Bioengineering and Biomedical Engineering from Arizona State University and a Bachelor of Engineering (B.E.) degree in Biomedical/Medical Engineering from Osmania University. Her tech stack includes ML, Research Scientist, Deep Learning, AWS, Data Scientist, Sagemaker, SVM, Python, XGBoost, Random Forest, NLP, Pytorch, Scikit, Keras, Tensorflow, Neural Networks, Big Data, Docker, Pandas, CNN, Text Classification, Hyperparameter, Perceptron, Tokenization, and Normalization.