Sr Data Scientist Resume

Candidate Pitch:

▪ 13+ years of Data Science & Data Modeling experience with a proven track record of developing and implementing various models which have significantly improved business revenue and user experience. ▪ Lead the Data Science team, which is responsible for design, research and modelling of Recommendation systems, Review auto-classification and analysis, Review topic modeling, Image sentiment-analysis, Machine Learning applied to logistics optimization problems, Deep Learning models for image recognition and labelling. ▪ Experience developing computer vision algorithms and applications. ▪ Develop data-driven insights & solutions with advanced analytics (machine learning, deep learning, Ai and statistical models) to extract insights from large data sources. ▪ Experienced with machine learning & Deep Learning algorithms such as logistic regression, random forest, Ada Boost XGBoost, KNN, SVM, ANN, Deep learning for Computer Vision, RNN, LSTM, linear regression, lasso regression, ridge regression and k-means. ▪ Developed predictive models using Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, Clustering, and Neural Networks. ▪ Developed NLP models for Topic Extraction, Sentiment Analysis/Search. ▪ Work with NLTK library to NLP data processing and finding the patterns. ▪ Used Attention Models, Transformers -BERT for Topic Extraction, NER and Language translation. ▪ Experienced with Feature engineering, Feature selection and Time series models. ▪ Experiences with Dataiku & DataRobot. ▪ Model Deployment & Monitoring (MLOPS). ▪ Implemented CI/CD pipelines to deploy the models on AWS & GCP. ▪ Experiences in experimental design and setting up A/B tests. ▪ Designs and evaluate the results of controlled experiments to optimize elements of our offering; advise others with respect to their experimental designs. ▪ Expertise in predictive modeling using both supervised and unsupervised learning techniques. ● Master of Computer Application, Vinayaka Mission University, 2016 ● AWS Certified Machine Learning – Specialty ● Power Bi Certified ● Python for Data Science Certified ● Microsoft Certified Technology Specialist: SQL Server ● Microsoft Certified Database Administrator: SQL Server ● ITIL V3 Foundation Technologies/Tools Machine Learning, Deep Learning, Machine/Computer Vision, Text Classification, Time Series, Natural Language Processing (NLP), Tensor flow, Theano, Keras, Python, SQL, Pyspark, Hugging face, fast.ai, CI/CD, AWS & GCP ML/Data Analysis Techniques Data exploration, Feature Engineering, Regression, Classification, Hypothesis Testing, Neural Network, Feature Extraction, Text Mining, Statistical Analysis, Natural Language Processing, Boosting, Bagging, Forecasting, A/B Testing Programming Languages Python, SQL, Pyspark Cloud Platform Amazon AWS, GCP, Heroku, Microsoft Azure, Snowflake Data Visualization Tableau, Matplotlib, Plotly, Seaborn, Powerbi Statistical Methods t-test, Chi-squared and ANOVA testing, A/B Testing, Descriptive and Inferential Statistics, Hypothesis testing Databases Microsoft SQL Server, Oracle, Teradata, Netezza, Snowflake Data Modelling Tools Erwin Rest APIs Fastapi, Flask Versioning MLFlow, Git ▪ Model Deployment & Monitoring (MLOPS). ▪ Implemented CI/CD pipelines to deploy the models on AWS & GCP. ▪ Experiences in experimental design and setting up A/B tests. ▪ Designs and evaluate the results of controlled experiments to optimize elements of our offering; advise others with respect to their experimental designs. ▪ Expertise in predictive modeling using both supervised and unsupervised learning techniques. ▪ Hands on experience in different framework’s like Keras, TensorFlow. ▪ Highly skilled in using Tableau, PowerBI visualization tool for creating dashboards. ▪ Work closely with customer's, cross-functional teams, software developers, and business teams in an Agile work environment to drive data model implementations and algorithms into practice. ▪ Motivating and mentoring a team of data scientists to grow their skills and careers while engaging with them in Writing code, finding datasets, pulling knowledge together as a resource to bring team up-to the speed.