Drop in a mail regarding any collaboration or any other help at swarnenduiitb2020@gmail.com Data science Professional with 6+ yrs experience in diverse use cases -Research interests with Ph.D. in computational Mechanics from IIT Bombay,and Supply Chain Analytics from Massachusetts Institute of Technology My dexterity lies in driving Data Science practice; analyzing problems and uncovering opportunities using predictive analysis. Demonstrated excellence in building frameworks & ideas for effective mining & analysis of data from multiple sources to drive improvement and delivered data-driven solutions to resolve business challenges. I am a programmer by heart who loves to code. Gradually with time, I have taken up greater responsibilities Deep learning, NLP and Machine learning. This involves building a Data Science Team, managing engineering practices within the team & ensuring quality of the software. Work and lead end-to-end projects on complete AI pipeline starting from Data Engineering >Data Science > ML Engineering, i.e. from Data preparation to model building till deployment. Quick formulation and designing of the Data Science problem statement. Hands-on experience of implementing end to end projects in NLP,Computer vision problems, Forecasting & Explainable AI. Technical Skills  ML UseCases: Credit Cards Fraud detection, AML Detection, Credit Scorecard Modeling, Net Promoter score NPS Prediction ,Anomaly Detection using various statistical and modeling techniques like Zscore,BCP,IsolationForest,Autoencoder Seo Query Intent Classification, Topic Modeling and Product Reviews summarization, Image Captioning, Telecom users behavioral segmentation and churn prediction  Cloud Services and MLOps: AWS Sagemaker Studio,AWS Lambda, AWS API Gatway, Model Deployment using Flak and AWS Sagemaker, Creating ML Pipelines(Model training,Inferencingdebugging,monitoring) in Sage maker,Model monitoring and Model Debugging in Sagemaker  Distributed Big Data ML Frameworks H2o with Python, Spark with Python using Pyspark  Machine Learning Algorithm’s Linear & Logistocs regression, Gradient Boosting Machine(GBM),XGBoost,Random Forest Support SVM,Linear and Logistic Regression,Ridge-Lasso Regression,CART, Kmeans and Density based clustering DBSCAN, Principle Component Analysis  NLP & Deep Learning Toolkit Word2Vec, Glove, spaCy, Nltk, Genism,Transformer, BERT,LDA, BertTopic,ANN,RNN,CNN,Transformer,BERT,LSTM,PyTorch,Tenserflow,Keras  Forecasting Techniques ARIMA,SARIMA,Prophet,AutoARIMA,LSTM  Programming Languages Python,R,SQL