About

Sumanth Meenan is a skilled Data Scientist with over 2 years of experience in building Machine Learning and Deep Learning models. He has a strong programming background in Python and R and is proficient in data visualization using Tableau. He has hands-on experience in building different Machine Learning and Deep Learning models, including regression, classification, clustering, and topic modeling. Sumanth's expertise extends to sampling techniques, deep learning, adversarial machine learning, time series forecasting, and recommendation systems. He is also well-versed in Natural Language Processing techniques such as tokenization, stemming, and lemmatization. He is proficient in working with frameworks such as Keras, Tensorflow, and Pytorch, and libraries such as OpenCV, Numpy, Pandas, Scikit-Learn, Matplotlib, Plotly, and Cufflinks. Sumanth has worked extensively on Python3, from web scraping on JS enabled web pages using BeautifulSoup and Selenium to building Machine Learning models using libraries like sklearn, libsvm, xgboost, nltk, and spacy. He has deployed these models in production using Flask. Currently working as a Data Scientist at Anthem, Inc., Sumanth has also worked with Taiger and Konverge.ai in the past. At Taiger, he developed multiple custom NER models for Banking, Finance & Healthcare data, worked on image pre-processing & information extraction projects, and designed NLP modules as microservices in Docker containers. At Konverge.ai, he worked on information extraction from unstructured documents, image segmentation, and object detection of pathology slides using Mask R-CNN, UNet, and Faster R-CNN. Sumanth holds a Bachelor of Technology degree from Visvesvaraya National Institute of Technology. He has also completed his 12th grade from Sri Chaitanya Junior Kalasala and his 10th grade from Dr.K.K.R's Gowtham Concept School. Overall, Sumanth has 5.20 years of relevant experience as a Data Scientist and Research Scientist. His technical expertise includes CNN, ML, SVM, XGBoost, Random Forest, Deep Learning, Scikit, NLP, Naive Bayes, Keras, RNN, Tokenization, Stemming, OpenCV, Selenium, Python, Tensorflow, Pandas, Flask, Pytorch, Docker, Microservices, Object detection, Object Detection, and Image Segmentation.